Application of ddPCR in the rapid diagnosis of bloodstream infections: detection of Staphylococcus, Enterococcus spp, Streptococcus spp and Candida spp

Application of ddPCR in the rapid diagnosis of bloodstream infections: detection of Staphylococcus, Enterococcus spp, Streptococcus spp and Candida spp | 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 Research Article Application of ddPCR in the rapid diagnosis of bloodstream infections: detection of Staphylococcus, Enterococcus spp, Streptococcus spp and Candida spp Liuyong You, Shuang Liu, Zhiwei Lin, Donghua Feng, Xiaojun Ou, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6320844/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The current gold standard for the detection of bloodstream infections has several limitations, including a low positivity rate, a high contamination rate, and a lengthy flow cycle. Droplet digital polymerase chain reaction (ddPCR), a novel technique offering high sensitivity and specificity and the ability to provide absolute quantification, has not been extensively investigated in the context of bloodstream infections. Methods A prospective study was conducted on 77 patients with suspected bloodstream infections, with blood cultures and ddPCR used to investigate the presence of four pathogens. Additionally, the general characteristics of the patients and relevant routine tests were collected and analysed to assess the diagnostic efficacy of ddPCR for bloodstream infections. Results 77 patients from intensive care medicine, respiratory medicine, and other departments considering BSIs were enrolled. Blood cultures identified 24 positive samples, including 8 Staphylococcus aureus, 12 Enterococcus species, 1 Streptococcus species, and 3 Candida species.. The ddPCR assay demonstrated 100% sensitivity for all four pathogen categories and specificities of 94.20%, 92.31%, 93.42%, and 94.59%, respectively. In addition, the ddPCR assay simultaneously detected 4 cases of polymicrobial infections and 14 cases of negative blood cultures, which was verified by Sanger sequencing. The ddPCR assay's detection time was significantly shorter than that of blood cultures, with an average of 4.31 ± 0.94 hours compared to 68.40 ± 2.50 hours ( p < 0.01). Conclusion The present study demonstrates that droplet digital polymerase chain reaction (ddPCR) significantly reduces the turnaround time of positive blood culture specimens. Furthermore, it has high sensitivity, specificity and negative predictive value, and enables early diagnosis of bloodstream infections. A larger number of samples is required to validate the correlation between absolute ddPCR quantification and patient severity and prognosis. Bloodstream infection (BSI) droplet digital polymerase chain reaction (ddPCR) sepsis pathogen detection rapid diagnostics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Bloodstream infections (BSIs), especially those causing sepsis, are an important public health problem worldwide 1 . Rapid and accurate identification of suspected bloodstream infections and precise use of antibiotics sensitive to the causative organisms is the best way to treat sepsis and improve patient outcomes 2 . Relevant studies have shown that sepsis mortality can range from25–80%.Furthermore, in sepsis patients, for every hour of delay in administering effective antibiotics within the first six hours of hemodynamic disturbances, the survival rate decreases by an average of 7.6%. ,, and the survival rate in severe sepsis decreases from 80–10% for failure to provideappropriate treatment within 24 hours 3 , 4 . For optimal treatment outcomes, current guidelines recommend starting accurate and effective antibiotic therapy as early as possible, preferably within 1 hour of recognition of sepsis or septic shock 5 , 6 . However, due to the limitations of current conventional culture methods with long culture cycles, low sensitivity and high false positives, identification and release of drug sensitivity results usually takes 2–5 days. This results in about 46% of bloodstream infections not being treated promptly and accurately, increasing the use of unnecessary broad-spectrum antibiotics by 50%, increasing mortality by about 35%, and dramatically increasing healthcare costs 7 – 9 . Therefore, the development of rapid, accurate and sensitive diagnostic tools, early and accurate diagnosis, and timely and effective treatment is crucial for improving the prognosis of patients with bloodstream infections and sepsis. Relevant studies have shown that the percentage of gram-positive cocci in patients with positive blood cultures can range from30-60%, with a significantly higher incidence in ICU and respiratory departments compared to other departments 10 , which suggests that critically ill patients are more susceptible to infection with gram-positive cocci, Furthermore,some studies have shown that BSIscaused by Staphylococcus aureus can be up to 10–20% 5 , and the majority of these Staphylococcus aureus infections are methicillin-resistant Staphylococcus aureus (MRSA) 11 , and the lethality of MRSA-associated BSIs is significantly higher than that of other bacterial infections 12 . In addition, Candida-associated BSIs account for about 5–15% of the associated BSIs, and are often accompanied by multiple infections predominate, which can lead to various complications, such as immune-compromised, invasive tube placement operations, etc., and the mortality rate associated with these infections is much higher than that of common bacterial infections 13 . However, at the same concentration, gram-positive cocci and fungi exhibitlower sensitivity and longer culture periods in blood cultures, resulting in delayedr use of rational antibiotics and higher mortality, therefore, early and rapid reporting of gram-positive cocci and fungi is of great significance in the diagnosis and treatment of clinical BSI 14 – 16 . Blood culture remains the gold standard for bloodstream infections, but Its limitations, including lengthy turnaround times, reduced sensitivity, and high morbidity and mortality rates, underscore the pressing requirement for early and prompt diagnostic instruments.Droplet digital polymerase chain reaction (ddPCR) emaerges as a promising technology, which involves dividing the template into uniformly sized droplets, amplifies them, and then counting the positive and negative reactions after amplification, and calculates the absolute copy number of the template using Poisson's statistic, which is expected to help diagnose BSIs through rapid detection of the pathogens and quantitative loading, as a new technology with high sensitivity and the ability to quantify the target molecules in absolute terms 17 , 18 . Currently ddPCR has been used in many clinical fields, for example, for detecting gene mutations related to solid tumours such as haematological tumours or lung adenocarcinomas, to provide a basis for clinical diagnosis and treatment 19 . In addition, ddPCR has also been widely cited in infectious diseases 20 .It has also been demonstrated that ddPCR can be used for the detection of some special pathogens that are difficult to culture, such as diagnosis of Mycobacterium tuberculosis, Plasmodium vivax, Leishmania protozoa, etc 20 , 21 . But the development of reagents specifically for detecting Gram-positive cocci and fungi, which are significant contributors to BSIs, remains relatively unexplored. sIn this study, we aimed to design and assess the accuracy of ddPCR in the diagnosis of common Gram-positive cocci and Candida-associated BSIs. We validated its concordance with conventional blood culture and Sanger sequencing in a prospective cohort of suspected BSI. Based on the latest data from the China Antimicrobial Drug Surveillance Network (CHINET ) ( http://www.chinets.com ) and the prevalent pathogens isolated in our institution, We designed and validated a novel combined test reagent for the three most common types of Gram-positive cocci and Candida, including Staphylococcus aureus, Enterococci (specifically Enterococcus faecalis and Enterococcus faecalis) , Streptococci (Such as Streptococcus pneumoniae, Streptococcus pharyngeus, Streptococcus pyogenes, Streptococcus bradyzoites , and Streptococcus anisopliae ), Candida species (including Candida albicans , Candida smooth , Candida nearly smooth , Candida tropicalis , and Candida crematoria ). Our objectives were to explore the potential of ddPCR in monitoring clinical disease progression and to further analyse the value of ddPCR in antimicrobial guidance in BSIs. 2. Materials and methods 2.1 Study Population and sample collection This study used multiplex ddPCR as a diagnostic tool for the evaluation of patients with suspected BSI associated with Gram-positive cocci and Candida. The study was conducted from 1 March 2024 to 31 June 2024 in the clinical laboratory of the First Hospital of Guangzhou Medical University. Inclusion criteria: age ≥ 18 years old and clinically suspected bloodstream infection with at least one or more of the following clinical manifestations or laboratory findings: (1) body temperature ≥ 38℃; (2) leukocyte elevation that is difficult to be explained by non-infectious factors (WBC > 12×10 9 /L); (3) PCT elevation that is difficult to be explained by non-infectious factors (PCT > 1.0 µg/L). Exclusion criteria: (1) Unavailability of samples or insufficient sample size; (2) Missing clinical samples; (3) Patients deemed unsuitable for enrolment by clinicians. After enrolment, ddPCR testing and blood culture testing were performed simultaneously (Fig. 1 ). Simultaneous testing of PCT, IL-6, and routine blood related tests were performed. Routine EDTA anticoagulant tubes were used to collect blood: standard venous blood collection procedures were followed to collect blood, not less than 6 mL, 3 mL for ddPCR experiments, and 3 mL of blood to separate plasma and extract nucleic acids for outgoing Sanger sequencing. Immediately after blood collection, the blood collection tube was gently inverted 10 times, centrifuged to separate plasma (1600xg, 5min), and nucleic acid extraction was carried out as soon as possible for immediate follow-up experiments or cryopreservation (-80°). Ethical approval was obtained from the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University, with approval number: ES-2024-120-02 7. Written informed consent was obtained from all participants prior to inclusion in the study. A schematic representation of the study can be found in Fig. 1 . 2.2 Blood cultures and pathogen identification Blood culture and pathogen identification Collected blood culture bottles were incubated at 37°C in BacT/ALERT VIRTUO ™ (BioMérieux). When the system reported a positive signal, Gram staining of the samples was performed, while the Colombian blood plates were separated and cultured (37°C, 5% CO 2 ) for identification by matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF-MS;BioMérieux). 2.3 ddPCR assay and Sanger sequencing Target pathogens were determined using the Pilot Gene Droplet Digital PCR System with a ddPCR master mix of 15 µ1 for each test group, which contained: ddPCR premix, forward and reverse primers, probe, and 5 µ1 of DNA template from isolated plasma, and the mixture was added to the respective channel feed cups of the digital PCR microtitre microchip. Droplets were generated using a droplet generator. The droplet-generated microarrays were amplified in a PCR amplifier and the droplets were analysed using an iScanner 5 microarray scanner (PilotGeneTechnology). Data analysis of droplet counts and amplitudes was performed using GenePMS software version v2.0.01.20011 (Fig. 2 ).DNA from 15 µ1 of the patients' isolated plasma was externally sent by Sanger sequencing to Sangong Bioengineering Co Ltd (Shanghai, China) for testing. 2.4 Data analysis Data were statistically analysed using IBM SPSS 22, continuous variables were expressed as median and interquartile distance, t-test was used for analytical statistics for normally distributed continuous variables, U-test was used for non-normally distributed continuous variables, chi-square test was used for categorical variables and p < 0.05 was considered statistically significant, and GraphPad was used to plot the correlation graphs. 3. Results 3.1 General characteristics of the patient cohort. Considering the exclusion/inclusion criteria, a total of 77 patients with suspected BSIs were recruited for this study (Table 1 ). The median age of the patients was 60.43 ± 17.9 years, of which 51 (66.23%) were male. The majority of patients hailed from ICU (32.5%, 25/77), followed by the respiratory department 12 (15.6%, 12/77), and other patients were from oncology, infectious diseases and haematology departments. Most of these patients were critically ill, 19 (24.7%, 19/77) of 37 (48.1%, 37/77) patients with lung infection were diagnosed with severe pneumonia, 17 (22.1%, 17/77) patients with tumors, with 9 (11.7%, 9/77) of them specifically suffering from lung tumors. In terms of inflammatory markers, the median levels of leukocyte count and calcitoninogen were 11.77 × 10 9 /L (IQR, 7.91, 17.51) and 0.68 µg/L (IQR, 0.22, 3.28), respectively. From the coagulation point of view, the median level of d-dimer was 2744 mg/dL (IQR, 1576, 4056). Furthermore,most patients exhibited mild anaemia with a median haemoglobin level of 94 g/L (IQR, 75, 166), indicating the severity and complexity of their conditions. Table 1 Clinical characteristics of recruited patients. Clinical Characteristics n = 77 Age, years 60.43 ± 17.90 Male, n (%) 66.23 Departments, n (%) ICU 25 (32.47) Respiratory medicine 12 (15.58) Cardiology department 4 (5.19) Gastrointestinal surgery 4 (5.19) Infectious Department 4 (5.19) Neurology department 3 (3.90) General internal medicine department 3 (3.90) Nephrology department 3 (3.90) Thoracic surgery 3 (3.90) Other Departments 16 (20.78) Lung infection 37 (48.05) Tumours 17 (22.07) Laboratory examination WBC, median (IQR) ×10 9 /L 11.77 (7.91, 17.51) HGB (g / L), median (IQR) 94 (75, 166) NEU, median (IQR) ×10 9 /L 9.6 (6.53, 14.04) NEU%, median (IQR) 84.2 (78.95, 89.5) EO, median (IQR) × 10 9 /L 0.01 (0, 0.07) LYM, median (IQR) × 10 9 /L 0.77 (0.42, 1.4) D-Dimer (ng/ml) 2744 (1576, 4056) PCT (ng/ml) 0.68 (0.22, 3.28) ICU :Intensive care unit; WBC : White blood cell; EOS : Eosinophils; NEU : Neutrophil; LYM : Lymphocyte; HGB : Glycosylated hemoglobin; PCT : Procalcitonin. 3.2 Evaluation of Blood Culture and ddPCR in Flora Detection and Statistics on Positive Reporting Time. Blood culture detected a total of 24 (31.2%, 24/77) positive specimens, including 8 (10.4%, 8/77) cases of Staphylococcus aureus , with an average time to report positive of 18.4 hours, 12 (15.6%, 12/77) cases of Enterococcus, with an average time positive reporting time of 20 hours, 1 (1.3%, 1/77) case of Streptococcus , with reporting timeof 11 hours, and 3 (3.9%, 3/77) cases of Candida. 3.9%, 3/77) cases, with an average reporting time of 30 h. The overall average blood culture reporting time for the tested specimens was 20.4 h (Fig. 3 ). In.contrast, a total of 38 (49.4%, 38/77) samples were positively detected by ddPCR, of which 20 (26.0%, 20/77) positive samples were fully consistent with the blood cultures, additionally, 4 samples were detected to contain bacteria or fungi other than those in the blood culture, and 14 samples tested negative by blood culture were found to contain bacteria or fungi by ddPCR..The ddPCR results were in complete agreement with Sanger sequencing, in addition, the average operation time of ddPCR was about 4h, and the time taken by ddPCR to identify pathogenic bacteria was significantly shorter than the time taken by blood culture to report positivity,and the difference between the two was statistically significant ( P < 0.01)(Fig. 3 ). 3.3 Comparison of the efficacy of ddPCR and blood culture tests In this study, blood culture was positive in 24 (31.2%, 24/77) of 77 samples, and within the detection range of ddPCR-targeted microorganisms, a total of 42 pathogens were detected by ddPCR in 38 (49.4%, 38/77) positive samples, of which a single bacterium or fungus was detected in 20 specimens, which was in complete agreement with blood culture and Sanger sequencing, and in four cases, two pathogens were detected in the specimens, and polymicrobial infection was considered, which was verified by Sanger sequencing results. ddPCR showed 100% sensitivity and 94.20%, 92.31%, 93.42% and 94.59% specificity for all four pathogens when compared to the blood culture, and the positive predictive value of ddPCR for Staphylococcus aureus and Enterococcus was higher at 66.67%, 70.59%, and the negative predictive value of ddPCR for blood cultures was 100%, suggesting that ddPCR may have significant potential for identifying patients with non-bloodstream infections, as well as a high positive predictive value for patients with BSI (Table 2 ). Table 2 Comparison of positive and negative agreement between ddPCR and BC. Type Sample (n = 77) BC+ BC- Sensitivity (100%) Specificity (100%) PPV (100%) NPV (100%) Total coincidence Rate (100%) Kappa Staphylococcus aureus ddPCR+ 8 4 100 94.20 66.67 100 94.81 0.772 ddPCR- 0 65 Enterococcus ddPCR+ 12 5 100 92.31 70.59 100 93.51 0.789 ddPCR- 0 60 Streptococcus ddPCR+ 1 5 100 93.42 16.67 100 93.51 0.269 ddPCR- 0 71 Candida ddPCR+ 3 4 100 94.59 42.86 100 93.51 0.577 ddPCR- 0 70 Total ddPCR+ 24 14 100 73.58 63.16 100 81.82 0.635 ddPCR- 0 39 PPV : positive predictive value; NPV : negative predictive value. 3.4 Comparison of clinical characteristics of patients who tested positive by different tests In this study, we summarised the clinical characteristics of 38 patients who tested positive by ddPCR. Compared with BC and ddPCR-positive patients, ddPCR-only positive patients, there were no statistically significant differences in age, sex, temperature, white blood cell count, haemoglobin content, eosinophils, lymphocyte count, D-dimer, 28-day mortality, and there was a statistically significant difference between the two in the indicator of infection, PCT ( p < 0.05), which was 0.75 in ddPCR-positive patients only ( 0.5, 0.96), which was lower than the PCT of 1.73 (0.35, 6.83) in double-positive patients (Table 3 ).Values are expressed as mean ± standard deviation or number of subjects (percentage of total columns), P values were calculated using the t-test for statistical differences between ddPCR positivity only versus simultaneous positivity for BC and ddPCR, and 28-day mortality was compared for differences using the fisher test (n < 40). Table 3 Comparison of clinical characteristics of patients who tested positive by different tests Positive patients based on BC and/or ddPCR n = 38 ddPCR only (+) n = 14 BC & ddPCR (+) n = 24 P Age 58.84 ± 18.89 53.29 ± 19.15 60.08 ± 17.52 0.158 Male n, (%) 24 (63.16) 8 (57.14) 16 (60.67) Temperature ℃ 37.99 ± 1.35 37.94 ± 1.54 38.02 ± 1.28 0.858 Expenditure 82105.18 (27667.31, 206853.06) 77853.73 (28582.30, 141467.83) 82105.18 (28133.98, 244729.51) 0.596 Laboratory examination WBC, median (IQR) ×10 9 /L 11.77 (7.2, 15.41) 14.16 (12.83, 19.02) 10.37 (6.75, 14.54) 0.283 HGB (g / L), median (IQR) 94 (73.5, 117.5) 105 (75.75, 122) 87.5 (72, 105.5) 0.259 NEU, median (IQR) ×10 9 /L 9.6 (5.99, 12.7) 12.48 (10.18, 13.1) 8.96 (5.57, 12.3) 0.183 NEU%, median (IQR) 84.2 (79.4, 88.5) 83.1 (78.6, 91.3) 84.9 (80.45, 88.35) 0.471 EOS, median (IQR) × 10 9 /L 0.02 (0, 0.07) 0.03 (0.01, 0.13) 0.01 (0, 0.05) 0.498 LYM, median (IQR) × 10 9 /L 0.79 (0.4, 1.3) 1.15 (0.4, 1.57) 0.74 (0.52, 1.2) 0.387 D-Dimer 3651.86 (1848.52, 5369) 2897 (1426, 3856.72) 3651.86 (2345.5, 6074.75) 0.267 PCT 0.92 (0.46, 3.81) 0.75 (0.5, 0.96) 1.73 (0.35, 6.83) 0.049 28-day mortality, n(%) 5 (13.16) 1 (7.14) 4 (16.67) 0.381 WBC : White blood cell; EOS : Eosinophils; NEU : Neutrophil; LYM : Lymphocyte; HGB : Glycosylated hemoglobin; PCT : Procalcitonin. 3.5 Analysis of patient prognosis and strain type There were 10 deaths in this study, five of which were negative for blood culture and ddPCR, one case of enterococci, one case of Staphylococcus aureus with ddPCR-positive blood culture-negative blood culture, in addition to the three patients with Candida-associated bloodstream infections with positive blood cultures and ddPCR-positive blood cultures died, resulting in a mortality rate of 100 per cent (Fig. 4 ). Patient mortality, improvement rate and 28-day in admission rate were not statistically significant differences in the results between groups ( P > 0.05), and the relevant literature (sample sizes of 16 and 22 cases, respectively) supports this experimental study, which may also be related to the small sample size 22 , 23 . 3.6 Analysis of drug sensitivity results in positive patients. The present study identified a total of eight strains of Staphylococcus aureus , twelve strains of enterococci (comprising six Enterococcus faecalis and six Enterococcus faecalis ), a single strain of Streptococcus , and three strains of Candida ( Pseudomona albicans, Candida glabrata, Candida tropicalis ). The results of the drug sensitivity tests are presented in the table (Fig. 5 ). Two cases of methicillin-resistant Staphylococcus aureus (MRSA) were identified among the S. aureus strains (25%, 2/8), and seven cases were β-lactamase-producing (87.5%, 7/8). The β-lactamase-negative strains were verified through borderline experiments. Ticlosanin, vancomycin, linezolid and daptomycin demonstrated 100% sensitivity in all eight cases of S. aureus. Of the 12 strains of enterococci , six were of the Enterococcus faecalis variety and six were of the Enterococcus faecalis variety. Additionally, three strains of vancomycin-resistant enterococci (VRE) were identified, representing 25% of the total number of enterococci strains (3/12). A significant proportion of the Enterococcus faecalis isolates were vancomycin-resistant, with a prevalence of 50%. All three strains of vancomycin-resistant Enterococcus (VRE) exhibited resistance to ticlosanin and sensitivity to linezolid. Four cases of high-level aminoglycoside resistance were identified, comprising three cases of E. faecalis and one case of E. faecalis. The streptococci exhibited bradyzoites, demonstrating sensitivity to penicillin G, vancomycin, linezolid, ceftriaxone, ceftizoxime, meropenem, tigecycline, and levofloxacin. The three cases of Candida were identified as Candida albicans , Candida smoothii , and Candida tropicalis . All three strains of the fungus demonstrated susceptibility to fluconazole (minimum inhibitory concentration (MIC) ≤ 1 mg/L), as per the Clinical and Laboratory Standards Institute (CLSI) guidelines. 4. Discussion Globally, bloodstream infections (BSIs) are the leading cause of morbidity and mortality in critically ill patients. Blood culture remains the gold standard for laboratory diagnosis of BSIs 24 . It is worth noting that there are some unavoidable problems with blood culture, and relevant studies have shown that the positive rate of blood culture is only about 70% 25 , and the volume of blood collected is a key factor in the recovery of pathogens from blood culture 26 , while the volume of blood inoculated also determines the sensitivity and specificity of blood culture.For adults, the recommended blood volume is about 38–40 ml/times of blood culture from both sides of the double set of blood culture, including aerobic and anaerobic bottles, in order to improve the sensitivity of blood culture and identify clinical contamination. It is generally believed that the positive rate decreases by 1% for every 1 ml decrease in the volume of blood, and the high demand for blood volume brings great trouble and low compliance to the clinical delivery of the test 27 . In addition, the reporting time of positive specimens is related to the bacterial concentration and strain in the blood, generally Gram-positive cocci and fungi take longer time to report positive than Gram-negative bacilli, and it takes at least 6–8 hours to culture colonies after reporting positive to identify strain type, and it takes longer for fungi and Gram-positive cocci, and the cycle of blood culture is generally 5–7 d. Even if the samples are collected in full compliance with the operation procedure, it is still difficult to avoid the contamination problem. Even when samples are collected in full accordance with the protocols, it is difficult to avoid contamination problems, and there is still a clinical contamination rate of 1–3% 27 . This creates an urgent need for timely and sensitive new methods to be applied in the diagnosis and differential diagnosis of bloodstream infections. Several studies have validated the accuracy and sensitivity of ddPCR in the diagnosis of infection-related diseases 28 .To overcome the drawbacks of blood culture in the diagnosis of BSI, we developed a culture-independent ddPCR method for the rapid and accurate identification of the four most common pathogens, including Staphylococcus aureus, Enterococcus, Streptococcus, and Candida, in blood samples from patients with suspected BSI. only 3 ml of anticoagulant blood is required in the ddPCR assay to isolate nucleic acids for detection, which greatly reduces the volume of blood collection and improves the quality of blood bacteria. This greatly reduces the amount of blood collection and improves compliance with blood bacterial testing. In addition, ddPCR can quickly and promptly issue a preliminary report for the clinic, with a negative predictive value of up to 100% within 4 hours, which suggests that clinically suspected bloodstream infections can be ruled out within 4 hours, providing a preliminary diagnosis of bloodstream infections for the clinic. In addition, studies have shown that for some critically ill patients with BSIs their signs and symptoms are usually non-specific, which poses a great challenge for the diagnosis and treatment of clinical BSIs, and it has been shown that about 35.0% of sepsis patients are culture-negative 29 , and the mortality rate of these BC-negative patients is significantly higher than that of regular patients. Therefore, there remains a need for other appropriate diagnostic methods to complement conventional BC to identify possible pathogens in BC-negative sepsis patients. In our study, ddPCR identified 14 patients with negative blood cultures along with 4 patients with dual infections compared to conventional blood culture methods, and the mortality and 28-day readmission rates of ddPCR-positive patients were not significantly different from those of BC-positive patients, suggesting that ddPCR 30 , 31 , as a complementary method to conventional blood cultures, may have higher sensitivity to identify possible BC-negative septicaemic patients, providing a basis for clinical treatment and more timely prediction of patient outcome. Relevant studies have shown that ddPCR technology as a novel, rapid, absolute quantitative nucleic acid detection technology has been applied in many aspects, for example, some studies have shown that ddPCR can be used for tumour-related gene mutations and post-treatment monitoring of tumours, and other relevant studies have shown that ddPCR has also been used in the detection of SARS, hepatitis viruses, and HIV viruses, and the absolute quantification of their infections can be explored by ddPCR. In addition, studies have shown that ddPCR has also been used in the detection of SARS, hepatitis viruses, HIV viruses, to investigate the severity of the infection and the healing of patients through ddPCR, and ddPCR can also play a very good role in the detection of some difficult to culture some of the pathogenic bacteria or parasites, such as Mycobacterium tuberculosis, Shreiman protozoa, Plasmodium falciparum, and so on. In addition, further studies have shown that the dynamic detection of the absolute value of the ddPCR copy of the long term, with the PCT and CRP white blood cell counts and so on, it was found that the BSI related ddPCR. The dynamic absolute value counts of ddPCR may have some correlation with the severity of inflammatory infections. ddPCR even provides accurate and quantitative pathogen load data in time, partly reflecting the severity of infection and inflammation Pathogen load is often closely related to the severity of BSI. The human immune system and antibiotic therapy kill invaded pathogens in BSIs, resulting in the release of nucleic acids from the pathogen into the bloodstream; this nucleic acid is thought to be part of circulating cfDNA. ddPCR assists in the diagnosis of sepsis by detecting free DNA in the bloodstream to assist in determining the presence or absence of free pathogens in the bloodstream of patients 32 . Our results showed that ddPCR could identify S. aureus, enterococci, streptococci and Candida in whole blood samples within 4 h. The sensitivity for each strain was 100%, and the specificity was above 90%, in addition to the lower line of detection for positive coccobacilli and Candida was 0.05 and 0.1 copies/µl, respectively. ddPCR not only identifies the blood culture based test patients who are positive, studies have shown that ddPCR has a positive rate of 30–50% for bloodstream infections compared to the 10–12% positive rate of traditional BC methods. However, it is worth noting that this is a limitation of this study and does not allow for the diagnosis of BSI by ddPCR positivity. firstly, as microbial DNA may be released from dead pathogens and can still detect pathogen nucleic acids two weeks after a negative report from routine blood cultures, the presence of microbial DNA in the blood does not necessarily indicate that there is a live microorganism in the blood stream, but may also However, it is undeniable that ddPCR does have higher sensitivity and specificity in negative prediction and positive specimen turnover than conventional BC methods. In addition, we found two methicillin-resistant Staphylococcus aureus strains in BC-positive S. aureus and two vancomycin-resistant enterococci in enterococci, which is of great significance for clinical treatment, but the ddPCR method was not involved in the present experiment, and some studies have shown that the drug-resistant characteristics of S. aureus can be preliminarily determined by detecting related genes such as the MecA/C genes 33 , which can provide guidance on drug use in the clinic. Some studies have shown that ddPCR can be used to preliminarily determine the drug resistance characteristics by detecting related genes such as MecA/C gene, which can provide clinical guidance for drug use 34 . In conclusion, ddPCR still cannot completely replace traditional blood culture, but due to its high sensitivity and specificity, it can be an important supplement to traditional blood culture for diagnosis and exclusion of sepsis. In conclusion, we have successfully developed a new ddPCR method to detect four major pathogens in patients with suspected BSI, and clinical validation showed that our method is higher than blood cultures in terms of specificity, sensitivity, and turnaround time, making it a promising method for early and accurate diagnosis of BSI. However, there are several limitations of this study that need to be mentioned, in this preliminary study we only evaluated the four categories of pathogens included, Gram-negative bacilli and viruses were not involved, and secondly, ddPCR, as a molecular method, can only detect nucleic acid fragments in the blood, and is not able to judge to determine whether it is a true bloodstream infection, so there is a need to add a variety of assays such as blood cultures and serological tests such as PCT, and the diagnosis of blood stream infections is complex and multiple assays are required. The diagnosis of bloodstream infections is complex, and the combined use of multiple testing methods can lead to a more rapid and accurate diagnosis of BSI and make the results more meaningful for clinical diagnosis and treatment. 5. Conclusion Our team has pioneered the use of multiplex ddPCR to test blood samples for four types of pathogens suspected to be BSIs. Clinical validation has demonstrated that our method outperforms blood cultures in terms of specificity, sensitivity, and turnaround time. ddPCR holds the potential to serve as a supplementary tool to traditional blood culture methods in the near future for diagnosing bloodstream infections. It can enhance the diagnostic process by providing timely and sensitive assistance in the diagnosis and differential diagnosis of bloodstream infections. Abbreviations BSI bloodstream infection ddPCR digital polymerase chain reaction assay WBC White blood cell NEU Neutrophil LYM Lymphocyte EOS Eosinophils IQR Interquartile range PCT Procalcitonin PPV positive predictive value NPV negative predictive value Declarations Ethics approval and consent to participate Ethical approval for the inclusion of human subjects was granted by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University, with approval codes 2022 No.121 and 2024 No. G-007. The patients/participants provided their written informed consent to participate in this study. Consent for publication Not applicable Clinical trial number Not applicable Availability of data and material The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors. Competing interests The authors have declared that no competing interest exists. Fundings The Chinese National Natural Science Foundation (81960023), National Key Research and Development Program of China (2023YFF1203800), Natural Science Foundation of Guangdong Province (2023A1515012917), Natural Science Foundation of Guangdong Province (2024A1515012830), State Key Laboratory Project (SKLRD-Z-202305), Research enhancement project of Guangzhou medical university (No.2024SRP074). Major clinical research project of Guangzhou Medical University (GMUCR2024-02009) Authors Contributions Liuyong You, Shuang Liu, Zhiwei Lin should be regarded as the first author. Conception and design of the research: Baoqing Sun, Yueting Jiang; Drafting the manuscript: Liuyong You, Shuang Liu. Article structure design: Shuang Liu, Zhiwei Lin; Statistical analysis: Liuyong You, Fengrong Chen, Ru Wang. Samples collection and detection: Liuyong You, Xiaojun Ou, Donghua Feng; Acquisition of data: Liuyong You, Xiaojun Ou; Experimental management: Liuyong You, Donghua Feng. All authors read and approved the final version of the manuscript. Acknowledgements We thank the laboratory Department of the First Affiliated Hospital of Guangzhou Medical University for providing the experimental environment. References Dunbar, S. A.; Gardner, C.; Das, S. Diagnosis and Management of Bloodstream Infections With Rapid, Multiplexed Molecular Assays. Frontiers in Cellular and Infection Microbiology 2022 , 12 . DOI: 10.3389/fcimb.2022.859935. John F, M.; Elda, R.; Hugh, W.; Gunter F, H.; Patrick N A, H.; David L, P. Long-term morbidity and mortality following bloodstream infection: A systematic literature review. J Infect 2018 , 77 (1). DOI: 10.1016/j.jinf.2018.03.005. Ricard, F.; Ignacio, M.-L.; Gary, P.; Tiffany M, O.; Sean, T.; R Phillip, D.; Antonio, A.; Christa, S.; Mitchell M, L. Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: results from a guideline-based performance improvement program. Crit Care Med 2014 , 42 (8). DOI: 10.1097/ccm.0000000000000330. Anand, K.; Daniel, R.; Kenneth E, W.; Bruce, L.; Joseph E, P.; Satendra, S.; Robert, S.; Daniel, F.; Sergio, Z.; Leo, T.; et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med 34 (6). Stephanie S, B.; Bereneice, M.; Susan R, S.; James H, D.; Nancy E, C.; Michael A, S.; Alice S, W.; Melvin P, W.; Edward B, L.; Donna M, W. Effectiveness of Practices To Increase Timeliness of Providing Targeted Therapy for Inpatients with Bloodstream Infections: a Laboratory Medicine Best Practices Systematic Review and Meta-analysis. Clin Microbiol Rev 2015 , 29 (1). DOI: 10.1128/cmr.00053-14. Vincent X, L.; Vikram, F.-S.; John D, G.; Jennifer M, B.; Theodore J, I.; Jay, B.; Gabriel J, E. The Timing of Early Antibiotics and Hospital Mortality in Sepsis. Am J Respir Crit Care Med 2017 , 196 (7). DOI: 10.1164/rccm.201609-1848OC. Timsit, J.; Ruppé, E.; Barbier, F.; Tabah, A.; Bassetti, M. Bloodstream infections in critically ill patients: an expert statement. Intensive care medicine 2020 , 46 (2), 266-284. DOI: 10.1007/s00134-020-05950-6. Cheng, M.; Stenstrom, R.; Paquette, K.; Stabler, S.; Akhter, M.; Davidson, A.; Gavric, M.; Lawandi, A.; Jinah, R.; Saeed, Z.; et al. Blood Culture Results Before and After Antimicrobial Administration in Patients With Severe Manifestations of Sepsis: A Diagnostic Study. Annals of internal medicine 2019 , 171 (8), 547-554. DOI: 10.7326/m19-1696. Jianfeng, X.; Hongliang, W.; Yan, K.; Lixin, Z.; Zhongmin, L.; Bingyu, Q.; Xiaochun, M.; Xiangyuan, C.; Dechang, C.; Weihua, L.; et al. The Epidemiology of Sepsis in Chinese ICUs: A National Cross-Sectional Survey. Crit Care Med 2019 , 48 (3). DOI: 10.1097/ccm.0000000000004155. Vincent, J.; Sakr, Y.; Singer, M.; Martin-Loeches, I.; Machado, F.; Marshall, J.; Finfer, S.; Pelosi, P.; Brazzi, L.; Aditianingsih, D.; et al. Prevalence and Outcomes of Infection Among Patients in Intensive Care Units in 2017. JAMA 2020 , 323 (15), 1478-1487. DOI: 10.1001/jama.2020.2717. Anthony D, B.; Carson K L, L.; Adam S, K.; Mallika, S.; Kevin, G.; Akhil, G.; Pranav, T.; Julien, S.; Olivier, D. C.; Isabella, S.; et al. Staphylococcus aureus bacteraemia mortality: a systematic review and meta-analysis. Clin Microbiol Infect 2022 , 28 (8). DOI: 10.1016/j.cmi.2022.03.015. Athena P, K.; Kelly, H.; James, B.; Yi, M.; Isaac, S.; Erin, E.; Joelle, N.; Marion A, K.; Ghinwa, D.; Susan, P.; et al. Vital Signs: Epidemiology and Recent Trends in Methicillin-Resistant and in Methicillin-Susceptible Staphylococcus aureus Bloodstream Infections - United States. MMWR Morb Mortal Wkly Rep 2019 , 68 (9). DOI: 10.15585/mmwr.mm6809e1. Seána, D.; Ines, L.; Kerstin, H.; Oliver, K. Host response to Candida albicans bloodstream infection and sepsis. Virulence 2015 , 6 (4). DOI: 10.4161/21505594.2014.988096. Cairns, K.; Udy, A.; Peel, T.; Abbott, I.; Dooley, M.; Peleg, A. Therapeutics for Vancomycin-Resistant Enterococcal Bloodstream Infections. Clinical microbiology reviews 2023 , 36 (2), e0005922. DOI: 10.1128/cmr.00059-22. Bai, A.; Lo, C.; Komorowski, A.; Suresh, M.; Guo, K.; Garg, A.; Tandon, P.; Senecal, J.; Del Corpo, O.; Stefanova, I.; et al. Staphylococcus aureus bacteraemia mortality: a systematic review and meta-analysis. Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases 2022 , 28 (8), 1076-1084. DOI: 10.1016/j.cmi.2022.03.015. Duggan, S.; Leonhardt, I.; Hünniger, K.; Kurzai, O. Host response to Candida albicans bloodstream infection and sepsis. Virulence 2015 , 6 (4), 316-326. DOI: 10.4161/21505594.2014.988096. Stephen J, S.; Keith R, J. Digital PCR-An Emerging Technology with Broad Applications in Microbiology. Clin Chem 2019 , 66 (1). DOI: 10.1373/clinchem.2019.304048. Danrui, W.; Shang, W.; Xiongfeng, D.; Qing, H.; Yue, L.; Zhujun, W.; Kai, F.; Yan, L.; Ye, D. ddPCR surpasses classical qPCR technology in quantitating bacteria and fungi in the environment. Mol Ecol Resour 2022 , 22 (7). DOI: 10.1111/1755-0998.13644. Dominic J, V.; Dhavan, S.; Amy, W.; Larissa, M.; Madison, C.; Lauren M, J.; John, A.; Kevin, D.; Arlene, D. S.; Grace, S.; et al. Mutant KRAS in Circulating Tumor DNA as a Biomarker in Localized Pancreatic Cancer in Patients Treated with Neoadjuvant Chemotherapy. Ann Surg 2024 , (0). DOI: 10.1097/sla.0000000000006562. Li, Y.; Huang, K.; Yin, J.; Tan, Z.; Zhou, M.; Dai, J.; Yi, B. Clinical evaluation of a multiplex droplet digital PCR for pathogen detection in critically ill COVID-19 patients with bloodstream infections. Infection 2023 , 52 (3), 1027-1039. DOI: 10.1007/s15010-023-02157-x. Xiaocui, W.; Guangkun, T.; Chunlei, S.; Yang, W.; Jinghui, Y.; Chunqiu, W.; Chaohui, H.; Fangyou, Y. Targeted next-generation sequencing - a promising approach in the diagnosis of Mycobacterium tuberculosis and drug resistance. Infection 2024 , (0). DOI: 10.1007/s15010-024-02411-w. Shao, Z.; Zhu, J.; Wei, Y.; Jin, J.; Zheng, Y.; Liu, J.; Zhang, R.; Sun, R.; Hu, B. Pathogen load and species monitored by droplet digital PCR in patients with bloodstream infections: A prospective case series study. BMC Infectious Diseases 2022 , 22 (1). DOI: 10.1186/s12879-022-07751-2. Ingrid, Z.; Sara, C.; Gunlög, R.; Theresa, E.; Paula, M.; Kristoffer, S. High nuc DNA load in whole blood is associated with sepsis, mortality and immune dysregulation in Staphylococcus aureus bacteraemia. Infect Dis (Lond) 2019 , 51 (3). DOI: 10.1080/23744235.2018.1562205. W V, K.; S, R. Burden of bacterial bloodstream infection-a brief update on epidemiology and significance of multidrug-resistant pathogens. Clin Microbiol Infect 2019 , 26 (2). DOI: 10.1016/j.cmi.2019.10.031. Towns, M.; Jarvis, W.; Hsueh, P. Guidelines on blood cultures. Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi 2010 , 43 (4), 347-349. DOI: 10.1016/s1684-1182(10)60054-0. Bae, M.; In Kim, H.; Park, J.; Ryu, B.; Chang, J.; Sung, H.; Jung, J.; Kim, M.; Kim, S.; Lee, S.; et al. Improvement of blood culture contamination rate, blood volume, and true positive rate after introducing a dedicated phlebotomy team. European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology 2019 , 38 (2), 325-330. DOI: 10.1007/s10096-018-3430-4. Doern, G.; Carroll, K.; Diekema, D.; Garey, K.; Rupp, M.; Weinstein, M.; Sexton, D. Practical Guidance for Clinical Microbiology Laboratories: A Comprehensive Update on the Problem of Blood Culture Contamination and a Discussion of Methods for Addressing the Problem. Clinical microbiology reviews 2019 , 33 (1). DOI: 10.1128/cmr.00009-19. Yannick, W.; Daisy, D.; Anke, C.; Hennie M J, R.; Heiman F, W.; Chantal P, B.-R.; René H, T. M.; Geert J A, W. Droplet digital polymerase chain reaction for rapid broad-spectrum detection of bloodstream infections. Microb Biotechnol 2019 , 13 (3). DOI: 10.1111/1751-7915.13491. Gupta, S.; Sakhuja, A.; Kumar, G.; McGrath, E.; Nanchal, R.; Kashani, K. Culture-Negative Severe Sepsis: Nationwide Trends and Outcomes. Chest 2016 , 150 (6), 1251-1259. DOI: 10.1016/j.chest.2016.08.1460. Ingrid, Z.; Per, J.; Per, O.; Paula, M.; Kristoffer, S. Quantitative data from the SeptiFast real-time PCR is associated with disease severity in patients with sepsis. BMC Infect Dis 2014 , 14 (0). DOI: 10.1186/1471-2334-14-155. Ziqiang, S.; Jingwen, Z.; Yanyan, W.; Jun, J.; Yang, Z.; Jingquan, L.; Run, Z.; Renhua, S.; Bangchuan, H. Pathogen load and species monitored by droplet digital PCR in patients with bloodstream infections: A prospective case series study. BMC Infect Dis 2022 , 22 (1). DOI: 10.1186/s12879-022-07751-2. Emily M, E.; Christiaan R, d. V.; Felicia, R.; Batu, S.-K.; Lawrence, P.; David, H.; Erick R, S.; Lily, B.; Nicholas, D.; Desiree H, H.; et al. Microbial Cell-Free DNA Identifies Etiology of Bloodstream Infections, Persists Longer Than Conventional Blood Cultures, and Its Duration of Detection Is Associated With Metastatic Infection in Patients With Staphylococcus aureus and Gram-Negative Bacteremia. Clin Infect Dis 2021 , 74 (11). DOI: 10.1093/cid/ciab742. Carlo, G.; Liselotte Diaz, H.; Hanna, B.; Tim, E.; Christian G, G.; Ole E, H.; Vincent, J.; Gunnar, K.; Danilo, L. F. W.; Jos, M.; et al. Staphylococcus aureus bloodstream infections: diverging trends of meticillin-resistant and meticillin-susceptible isolates, EU/EEA, 2005 to 2018. Euro Surveill 2021 , 26 (46). DOI: 10.2807/1560-7917.Es.2021.26.46.2002094. Wu, J.; Tang, B.; Qiu, Y.; Tan, R.; Liu, J.; Xia, J.; Zhang, J.; Huang, J.; Qu, J.; Sun, J.; et al. Clinical validation of a multiplex droplet digital PCR for diagnosing suspected bloodstream infections in ICU practice: a promising diagnostic tool. Critical Care 2022 , 26 (1). DOI: 10.1186/s13054-022-04116-8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-6320844","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441209839,"identity":"bc95d001-c233-4e79-bc2c-bf3a4718306a","order_by":0,"name":"Liuyong You","email":"","orcid":"","institution":"Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liuyong","middleName":"","lastName":"You","suffix":""},{"id":441209840,"identity":"13067f46-3d9d-4993-bf19-eff2fe5f9959","order_by":1,"name":"Shuang Liu","email":"","orcid":"","institution":"Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Liu","suffix":""},{"id":441209841,"identity":"fa258336-10b7-44e4-b32a-7c2a9be7ad5d","order_by":2,"name":"Zhiwei Lin","email":"","orcid":"","institution":"Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhiwei","middleName":"","lastName":"Lin","suffix":""},{"id":441209842,"identity":"455910de-8401-4572-aca0-790cb12f4cb3","order_by":3,"name":"Donghua Feng","email":"","orcid":"","institution":"Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Donghua","middleName":"","lastName":"Feng","suffix":""},{"id":441209843,"identity":"2406102b-5ec4-4efa-8ca6-6370471e0bcd","order_by":4,"name":"Xiaojun Ou","email":"","orcid":"","institution":"Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Ou","suffix":""},{"id":441209844,"identity":"c7c41a6e-4e09-45f7-b8b8-ab4a5ab1cc69","order_by":5,"name":"Fengrong Chen","email":"","orcid":"","institution":"Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fengrong","middleName":"","lastName":"Chen","suffix":""},{"id":441209845,"identity":"bef818b0-effa-4747-9f23-8c54110e021b","order_by":6,"name":"Ru Wang","email":"","orcid":"","institution":"Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ru","middleName":"","lastName":"Wang","suffix":""},{"id":441209846,"identity":"b5c47d96-1b89-4852-8ab5-b59b307e3d32","order_by":7,"name":"Yueting Jiang","email":"","orcid":"","institution":"Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yueting","middleName":"","lastName":"Jiang","suffix":""},{"id":441209847,"identity":"0cd37cf4-7749-41a6-9803-b68e55421fb6","order_by":8,"name":"Baoqing Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIie3PMWsCMRTA8XcEbopkzdGD+wpPCneIgl8lN7mknR0Dgi5+gPQ7dCuIYw6hU4rrjSeuCrqUdijUnHPjjULzHx4Z3o8kAKHQfRa7QQGIAXE5MKY6k1g4kibadCNOoZsjVMK/n+mn9+ZrOkwL9vrJd2sHTXQ6y78J1s+T/tJO6EAfV7y0QAuiSPKy8hAuc96bbyjWH29YzoEOlIlJz0MyLfPkpyV23xI0wk+glvlDe8t2GTWdCNpD8Zhe/oJ13G9Ky2miq5n3L9lC5rvDdDjG7aapvtejMWOz6nT2PewaAeACIFIc3Ly5fyXMdF0OhUKh/9YvYrhQezDRx/QAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Baoqing","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-03-27 13:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6320844/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6320844/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82075599,"identity":"12c64b34-1304-4a17-90a6-c125b38e5ee0","added_by":"auto","created_at":"2025-05-06 13:45:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":246524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of blood screening criteria and results analysis. \u003c/strong\u003eThe flow chart of testing and analysis included the time of enrolment, enrolment criteria, exclusion criteria, collection of relevant clinical information of patients such as leukocyte count, PCT, etc. for subsequent statistical analyses, and the number of cases of ddPCRpositive and blood culturepositive patients were counted at the same time, respectively. BC+: Positive blood culture results: BC-: Negative blood culture results; ddPCR+: positive result of digital polymerase chain reaction assay; ddPCR− : negative result of digital polymerase chain reaction assay.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6320844/v1/470ad3cb81865969d90333a8.jpeg"},{"id":82071570,"identity":"2bd625d5-2c93-4dbd-9646-aa431952142c","added_by":"auto","created_at":"2025-05-06 13:21:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":466355,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eddPCR and BC detection process schematic diagram. (A).\u003c/strong\u003eRepresents an illustration of the simple operating procedure of ddPCR, including sample collection, nucleic acid amplification, droplet preparation, and statistical analysis;\u003cstrong\u003e (B).\u003c/strong\u003e Represents the basicoperating procedure of conventional blood culture, including sample collection, instrumental culture, Gram smear and staining, microscopic examination, preliminary reporting, identification and drug sensitization, and final report.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6320844/v1/4422e1115cbfc8e9141217be.png"},{"id":82071575,"identity":"9645e283-f5b9-4fd2-9fc3-1437f235413d","added_by":"auto","created_at":"2025-05-06 13:21:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":366257,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eddPCR with analysis of positive blood culture results .(A).\u003c/strong\u003eComparison of time to positive report between ddPCR and blood culture；\u003cstrong\u003e(B).\u003c/strong\u003eDistribution of ddPCR and blood culture positive pathogens；\u003cstrong\u003e(C).\u003c/strong\u003eAnalysis of the ratio of ddPCR to blood culture-positive pathogens；\u003cstrong\u003e(D).\u003c/strong\u003eComparison of ddPCR and blood culture positive detection process times.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6320844/v1/014e47953fde17ca61bd2124.png"},{"id":82071563,"identity":"76556763-3a9a-4aa5-ac34-1864873ab589","added_by":"auto","created_at":"2025-05-06 13:21:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98925,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical representation of blood culture and ddPCR results in patients who died, improved and were readmitted at 28 days. (A).\u003c/strong\u003eMortality analysis; \u003cstrong\u003e(B).\u003c/strong\u003eImprovement rate analysis;\u003cstrong\u003e(C).\u003c/strong\u003e Analysis of 28-day readmission rates;\u003cstrong\u003e(D).\u003c/strong\u003eDistribution of pathogens in fatal cases; \u003cstrong\u003e(E).\u003c/strong\u003eDistribution of pathogens in 28-day readmission cases.28-day readmission rates :patients who survived without improvement and who were not discharged from the hospital after more than 28 days were counted as 28-day readmissions.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6320844/v1/200f6133a810b25dbe3c1d30.png"},{"id":82071572,"identity":"d03886ab-a3af-4bbd-96ae-9cba06febd4d","added_by":"auto","created_at":"2025-05-06 13:21:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":294997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of drug sensitivity tests. (A).\u003c/strong\u003e \u003cem\u003eStaphylococcus aureus\u003c/em\u003e drug sensitivity statistics; \u003cstrong\u003e(B).\u003c/strong\u003e \u003cem\u003eEnterococcus\u003c/em\u003edrug sensitivity statistics. The drug sensitivity results are referenced to the Criteria for the Implementation of Antimicrobial Drug Susceptibility, M100, 34th edition (CLSI, M100, 34th edition). \u003cstrong\u003eMRSA:\u003c/strong\u003e Methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e; \u003cstrong\u003eVRE:\u003c/strong\u003e Vancomycin-resistant \u003cem\u003eEnterococcus.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6320844/v1/2953a778762e5fe2bfb37c5b.png"},{"id":82076536,"identity":"7083e72e-adbd-4b5a-af08-c315656e7740","added_by":"auto","created_at":"2025-05-06 13:53:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2793558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6320844/v1/2da11ff3-7634-4d1d-a73e-4701de921e0d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of ddPCR in the rapid diagnosis of bloodstream infections: detection of Staphylococcus, Enterococcus spp, Streptococcus spp and Candida spp","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBloodstream infections (BSIs), especially those causing sepsis, are an important public health problem worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Rapid and accurate identification of suspected bloodstream infections and precise use of antibiotics sensitive to the causative organisms is the best way to treat sepsis and improve patient outcomes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Relevant studies have shown that sepsis mortality can range from25\u0026ndash;80%.Furthermore, in sepsis patients, for every hour of delay in administering effective antibiotics within the first six hours of hemodynamic disturbances, the survival rate decreases by an average of 7.6%. ,, and the survival rate in severe sepsis decreases from 80\u0026ndash;10% for failure to provideappropriate treatment within 24 hours\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. For optimal treatment outcomes, current guidelines recommend starting accurate and effective antibiotic therapy as early as possible, preferably within 1 hour of recognition of sepsis or septic shock\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, due to the limitations of current conventional culture methods with long culture cycles, low sensitivity and high false positives, identification and release of drug sensitivity results usually takes 2\u0026ndash;5 days. This results in about 46% of bloodstream infections not being treated promptly and accurately, increasing the use of unnecessary broad-spectrum antibiotics by 50%, increasing mortality by about 35%, and dramatically increasing healthcare costs\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Therefore, the development of rapid, accurate and sensitive diagnostic tools, early and accurate diagnosis, and timely and effective treatment is crucial for improving the prognosis of patients with bloodstream infections and sepsis.\u003c/p\u003e \u003cp\u003eRelevant studies have shown that the percentage of gram-positive cocci in patients with positive blood cultures can range from30-60%, with a significantly higher incidence in ICU and respiratory departments compared to other departments\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, which suggests that critically ill patients are more susceptible to infection with gram-positive cocci, Furthermore,some studies have shown that BSIscaused by Staphylococcus aureus can be up to 10\u0026ndash;20%\u003csup\u003e5\u003c/sup\u003e, and the majority of these Staphylococcus aureus infections are methicillin-resistant Staphylococcus aureus (MRSA) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and the lethality of MRSA-associated BSIs is significantly higher than that of other bacterial infections\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In addition, Candida-associated BSIs account for about 5\u0026ndash;15% of the associated BSIs, and are often accompanied by multiple infections predominate, which can lead to various complications, such as immune-compromised, invasive tube placement operations, etc., and the mortality rate associated with these infections is much higher than that of common bacterial infections\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, at the same concentration, gram-positive cocci and fungi exhibitlower sensitivity and longer culture periods in blood cultures, resulting in delayedr use of rational antibiotics and higher mortality, therefore, early and rapid reporting of gram-positive cocci and fungi is of great significance in the diagnosis and treatment of clinical BSI\u003csup\u003e\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBlood culture remains the gold standard for bloodstream infections, but Its limitations, including lengthy turnaround times, reduced sensitivity, and high morbidity and mortality rates, underscore the pressing requirement for early and prompt diagnostic instruments.Droplet digital polymerase chain reaction (ddPCR) emaerges as a promising technology, which involves dividing the template into uniformly sized droplets, amplifies them, and then counting the positive and negative reactions after amplification, and calculates the absolute copy number of the template using Poisson's statistic, which is expected to help diagnose BSIs through rapid detection of the pathogens and quantitative loading, as a new technology with high sensitivity and the ability to quantify the target molecules in absolute terms\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Currently ddPCR has been used in many clinical fields, for example, for detecting gene mutations related to solid tumours such as haematological tumours or lung adenocarcinomas, to provide a basis for clinical diagnosis and treatment\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In addition, ddPCR has also been widely cited in infectious diseases\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.It has also been demonstrated that ddPCR can be used for the detection of some special pathogens that are difficult to culture, such as diagnosis of Mycobacterium tuberculosis, Plasmodium vivax, Leishmania protozoa, etc\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. But the development of reagents specifically for detecting Gram-positive cocci and fungi, which are significant contributors to BSIs, remains relatively unexplored. sIn this study, we aimed to design and assess the accuracy of ddPCR in the diagnosis of common Gram-positive cocci and Candida-associated BSIs. We validated its concordance with conventional blood culture and Sanger sequencing in a prospective cohort of suspected BSI. Based on the latest data from the China Antimicrobial Drug Surveillance Network (CHINET ) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.chinets.com\u003c/span\u003e\u003cspan address=\"http://www.chinets.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the prevalent pathogens isolated in our institution, We designed and validated a novel combined test reagent for the three most common types of Gram-positive cocci and Candida, including \u003cem\u003eStaphylococcus aureus, Enterococci\u003c/em\u003e (specifically \u003cem\u003eEnterococcus faecalis\u003c/em\u003e and\u003cem\u003eEnterococcus faecalis)\u003c/em\u003e, \u003cem\u003eStreptococci\u003c/em\u003e (Such as \u003cem\u003eStreptococcus pneumoniae, Streptococcus pharyngeus, Streptococcus pyogenes, Streptococcus bradyzoites\u003c/em\u003e, and \u003cem\u003eStreptococcus anisopliae\u003c/em\u003e), \u003cem\u003eCandida\u003c/em\u003e species (including \u003cem\u003eCandida albicans\u003c/em\u003e, \u003cem\u003eCandida smooth\u003c/em\u003e, \u003cem\u003eCandida nearly smooth\u003c/em\u003e, \u003cem\u003eCandida tropicalis\u003c/em\u003e, and \u003cem\u003eCandida crematoria\u003c/em\u003e). Our objectives were to explore the potential of ddPCR in monitoring clinical disease progression and to further analyse the value of ddPCR in antimicrobial guidance in BSIs.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population and sample collection\u003c/h2\u003e \u003cp\u003eThis study used multiplex ddPCR as a diagnostic tool for the evaluation of patients with suspected BSI associated with Gram-positive cocci and Candida. The study was conducted from 1 March 2024 to 31 June 2024 in the clinical laboratory of the First Hospital of Guangzhou Medical University. Inclusion criteria: age\u0026thinsp;\u0026ge;\u0026thinsp;18 years old and clinically suspected bloodstream infection with at least one or more of the following clinical manifestations or laboratory findings: \u003cb\u003e(1)\u003c/b\u003e body temperature\u0026thinsp;\u0026ge;\u0026thinsp;38℃; \u003cb\u003e(2)\u003c/b\u003e leukocyte elevation that is difficult to be explained by non-infectious factors (WBC\u0026thinsp;\u0026gt;\u0026thinsp;12\u0026times;10\u003csup\u003e9\u003c/sup\u003e /L); \u003cb\u003e(3)\u003c/b\u003e PCT elevation that is difficult to be explained by non-infectious factors (PCT\u0026thinsp;\u0026gt;\u0026thinsp;1.0 \u0026micro;g/L). Exclusion criteria: \u003cb\u003e(1)\u003c/b\u003e Unavailability of samples or insufficient sample size; \u003cb\u003e(2)\u003c/b\u003e Missing clinical samples; \u003cb\u003e(3)\u003c/b\u003e Patients deemed unsuitable for enrolment by clinicians. After enrolment, ddPCR testing and blood culture testing were performed simultaneously (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Simultaneous testing of PCT, IL-6, and routine blood related tests were performed. Routine EDTA anticoagulant tubes were used to collect blood: standard venous blood collection procedures were followed to collect blood, not less than 6 mL, 3 mL for ddPCR experiments, and 3 mL of blood to separate plasma and extract nucleic acids for outgoing Sanger sequencing. Immediately after blood collection, the blood collection tube was gently inverted 10 times, centrifuged to separate plasma (1600xg, 5min), and nucleic acid extraction was carried out as soon as possible for immediate follow-up experiments or cryopreservation (-80\u0026deg;).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e was obtained from the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University, with approval number: ES-2024-120-02 7. Written informed consent was obtained from all participants prior to inclusion in the study. A schematic representation of the study can be found in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Blood cultures and pathogen identification\u003c/h2\u003e \u003cp\u003eBlood culture and pathogen identification Collected blood culture bottles were incubated at 37\u0026deg;C in BacT/ALERT VIRTUO\u003csup\u003e\u0026trade;\u003c/sup\u003e (BioM\u0026eacute;rieux). When the system reported a positive signal, Gram staining of the samples was performed, while the Colombian blood plates were separated and cultured (37\u0026deg;C, 5% CO\u003csub\u003e2\u003c/sub\u003e) for identification by matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF-MS;BioM\u0026eacute;rieux).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 ddPCR assay and Sanger sequencing\u003c/h2\u003e \u003cp\u003eTarget pathogens were determined using the Pilot Gene Droplet Digital PCR System with a ddPCR master mix of 15 \u0026micro;1 for each test group, which contained: ddPCR premix, forward and reverse primers, probe, and 5 \u0026micro;1 of DNA template from isolated plasma, and the mixture was added to the respective channel feed cups of the digital PCR microtitre microchip. Droplets were generated using a droplet generator. The droplet-generated microarrays were amplified in a PCR amplifier and the droplets were analysed using an iScanner 5 microarray scanner (PilotGeneTechnology). Data analysis of droplet counts and amplitudes was performed using GenePMS software version v2.0.01.20011 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).DNA from 15 \u0026micro;1 of the patients' isolated plasma was externally sent by Sanger sequencing to Sangong Bioengineering Co Ltd (Shanghai, China) for testing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cp\u003eData were statistically analysed using IBM SPSS 22, continuous variables were expressed as median and interquartile distance, t-test was used for analytical statistics for normally distributed continuous variables, U-test was used for non-normally distributed continuous variables, chi-square test was used for categorical variables and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant, and GraphPad was used to plot the correlation graphs.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 General characteristics of the patient cohort.\u003c/h2\u003e \u003cp\u003eConsidering the exclusion/inclusion criteria, a total of 77 patients with suspected BSIs were recruited for this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median age of the patients was 60.43\u0026thinsp;\u0026plusmn;\u0026thinsp;17.9 years, of which 51 (66.23%) were male. The majority of patients hailed from ICU (32.5%, 25/77), followed by the respiratory department 12 (15.6%, 12/77), and other patients were from oncology, infectious diseases and haematology departments. Most of these patients were critically ill, 19 (24.7%, 19/77) of 37 (48.1%, 37/77) patients with lung infection were diagnosed with severe pneumonia, 17 (22.1%, 17/77) patients with tumors, with 9 (11.7%, 9/77) of them specifically suffering from lung tumors. In terms of inflammatory markers, the median levels of leukocyte count and calcitoninogen were 11.77 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/L (IQR, 7.91, 17.51) and 0.68 \u0026micro;g/L (IQR, 0.22, 3.28), respectively. From the coagulation point of view, the median level of d-dimer was 2744 mg/dL (IQR, 1576, 4056). Furthermore,most patients exhibited mild anaemia with a median haemoglobin level of 94 g/L (IQR, 75, 166), indicating the severity and complexity of their conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of recruited patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;77\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.43\u0026thinsp;\u0026plusmn;\u0026thinsp;17.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepartments, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (32.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (15.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiology department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (5.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrointestinal surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (5.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious Department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (5.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeurology department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral internal medicine department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNephrology department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThoracic surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (3.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Departments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (20.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (48.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (22.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaboratory examination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, median (IQR) \u0026times;10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.77 (7.91, 17.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB (g / L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (75, 166)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEU, median (IQR) \u0026times;10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6 (6.53, 14.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEU%, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.2 (78.95, 89.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEO, median (IQR) \u0026times; 10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01 (0, 0.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYM, median (IQR) \u0026times; 10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77 (0.42, 1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-Dimer (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2744 (1576, 4056)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.22, 3.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eICU\u003c/b\u003e:Intensive care unit;\u003cb\u003eWBC\u003c/b\u003e: White blood cell; \u003cb\u003eEOS\u003c/b\u003e: Eosinophils; \u003cb\u003eNEU\u003c/b\u003e: Neutrophil; \u003cb\u003eLYM\u003c/b\u003e: Lymphocyte; \u003cb\u003eHGB\u003c/b\u003e: Glycosylated hemoglobin; \u003cb\u003ePCT\u003c/b\u003e: \u003cem\u003eProcalcitonin.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Evaluation of Blood Culture and ddPCR in Flora Detection and Statistics on Positive Reporting Time.\u003c/h2\u003e \u003cp\u003eBlood culture detected a total of 24 (31.2%, 24/77) positive specimens, including 8 (10.4%, 8/77) cases of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, with an average time to report positive of 18.4 hours, 12 (15.6%, 12/77) cases of Enterococcus, with an average time positive reporting time of 20 hours, 1 (1.3%, 1/77) case of \u003cem\u003eStreptococcus\u003c/em\u003e, with reporting timeof 11 hours, and 3 (3.9%, 3/77) cases of Candida. 3.9%, 3/77) cases, with an average reporting time of 30 h. The overall average blood culture reporting time for the tested specimens was 20.4 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn.contrast, a total of 38 (49.4%, 38/77) samples were positively detected by ddPCR, of which 20 (26.0%, 20/77) positive samples were fully consistent with the blood cultures, additionally, 4 samples were detected to contain bacteria or fungi other than those in the blood culture, and 14 samples tested negative by blood culture were found to contain bacteria or fungi by ddPCR..The ddPCR results were in complete agreement with Sanger sequencing, in addition, the average operation time of ddPCR was about 4h, and the time taken by ddPCR to identify pathogenic bacteria was significantly shorter than the time taken by blood culture to report positivity,and the difference between the two was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01)(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Comparison of the efficacy of ddPCR and blood culture tests\u003c/h2\u003e \u003cp\u003e In this study, blood culture was positive in 24 (31.2%, 24/77) of 77 samples, and within the detection range of ddPCR-targeted microorganisms, a total of 42 pathogens were detected by ddPCR in 38 (49.4%, 38/77) positive samples, of which a single bacterium or fungus was detected in 20 specimens, which was in complete agreement with blood culture and Sanger sequencing, and in four cases, two pathogens were detected in the specimens, and polymicrobial infection was considered, which was verified by Sanger sequencing results. ddPCR showed 100% sensitivity and 94.20%, 92.31%, 93.42% and 94.59% specificity for all four pathogens when compared to the blood culture, and the positive predictive value of ddPCR for Staphylococcus aureus and Enterococcus was higher at 66.67%, 70.59%, and the negative predictive value of ddPCR for blood cultures was 100%, suggesting that ddPCR may have significant potential for identifying patients with non-bloodstream infections, as well as a high positive predictive value for patients with BSI (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of positive and negative agreement between ddPCR and BC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample (n\u0026thinsp;=\u0026thinsp;77)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBC+\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBC-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity (100%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity (100%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTotal coincidence\u003c/p\u003e \u003cp\u003eRate\u003c/p\u003e \u003cp\u003e(100%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eKappa\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eddPCR+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e94.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e66.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e94.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eddPCR-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eEnterococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eddPCR+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e92.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e70.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e93.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eddPCR-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eddPCR+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e93.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e93.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eddPCR-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eCandida\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eddPCR+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e94.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e42.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e93.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eddPCR-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eddPCR+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e73.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e63.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e81.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eddPCR-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePPV\u003c/b\u003e: positive predictive value; \u003cb\u003eNPV\u003c/b\u003e: negative predictive value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Comparison of clinical characteristics of patients who tested positive by different tests\u003c/h2\u003e \u003cp\u003eIn this study, we summarised the clinical characteristics of 38 patients who tested positive by ddPCR. Compared with BC and ddPCR-positive patients, ddPCR-only positive patients, there were no statistically significant differences in age, sex, temperature, white blood cell count, haemoglobin content, eosinophils, lymphocyte count, D-dimer, 28-day mortality, and there was a statistically significant difference between the two in the indicator of infection, PCT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which was 0.75 in ddPCR-positive patients only ( 0.5, 0.96), which was lower than the PCT of 1.73 (0.35, 6.83) in double-positive patients (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).Values are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or number of subjects (percentage of total columns), P values were calculated using the t-test for statistical differences between ddPCR positivity only versus simultaneous positivity for BC and ddPCR, and 28-day mortality was compared for differences using the fisher test (n\u0026thinsp;\u0026lt;\u0026thinsp;40).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical characteristics of patients who tested positive by different tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive patients based\u003c/p\u003e \u003cp\u003eon BC and/or ddPCR\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;38\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eddPCR only (+)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBC \u0026amp; ddPCR (+)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;24\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.84\u0026thinsp;\u0026plusmn;\u0026thinsp;18.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.29\u0026thinsp;\u0026plusmn;\u0026thinsp;19.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.08\u0026thinsp;\u0026plusmn;\u0026thinsp;17.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale n, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (63.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (57.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (60.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature ℃\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpenditure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82105.18 (27667.31, 206853.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77853.73 (28582.30, 141467.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82105.18 (28133.98, 244729.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory examination\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, median (IQR) \u0026times;10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.77 (7.2, 15.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.16 (12.83, 19.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.37 (6.75, 14.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB (g / L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (73.5, 117.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (75.75, 122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.5 (72, 105.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEU, median (IQR) \u0026times;10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.6 (5.99, 12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.48 (10.18, 13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.96 (5.57, 12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEU%, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.2 (79.4, 88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.1 (78.6, 91.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.9 (80.45, 88.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOS, median (IQR) \u0026times; 10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02 (0, 0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 (0.01, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01 (0, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYM, median (IQR) \u0026times; 10\u003csup\u003e9\u003c/sup\u003e /L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.4, 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15 (0.4, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74 (0.52, 1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-Dimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3651.86 (1848.52, 5369)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2897 (1426, 3856.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3651.86 (2345.5, 6074.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.46, 3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75 (0.5, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73 (0.35, 6.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day mortality, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (13.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (7.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (16.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eWBC\u003c/b\u003e: White blood cell; \u003cb\u003eEOS\u003c/b\u003e: Eosinophils; \u003cb\u003eNEU\u003c/b\u003e: Neutrophil; \u003cb\u003eLYM\u003c/b\u003e: Lymphocyte; \u003cb\u003eHGB\u003c/b\u003e: Glycosylated hemoglobin; \u003cb\u003ePCT\u003c/b\u003e: \u003cem\u003eProcalcitonin.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Analysis of patient prognosis and strain type\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere were 10 deaths in this study, five of which were negative for blood culture and ddPCR, one case of enterococci, one case of Staphylococcus aureus with ddPCR-positive blood culture-negative blood culture, in addition to the three patients with Candida-associated bloodstream infections with positive blood cultures and ddPCR-positive blood cultures died, resulting in a mortality rate of 100 per cent (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Patient mortality, improvement rate and 28-day in admission rate were not statistically significant differences in the results between groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and the relevant literature (sample sizes of 16 and 22 cases, respectively) supports this experimental study, which may also be related to the small sample size\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Analysis of drug sensitivity results in positive patients.\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe present study identified a total of eight strains of \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, twelve strains of \u003cem\u003eenterococci\u003c/em\u003e (comprising six \u003cem\u003eEnterococcus faecalis\u003c/em\u003e and six \u003cem\u003eEnterococcus faecalis\u003c/em\u003e), a single strain of \u003cem\u003eStreptococcus\u003c/em\u003e, and three strains of \u003cem\u003eCandida\u003c/em\u003e (\u003cem\u003ePseudomona albicans, Candida glabrata, Candida tropicalis\u003c/em\u003e). The results of the drug sensitivity tests are presented in the table (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Two cases of methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (MRSA) were identified among the S. aureus strains (25%, 2/8), and seven cases were β-lactamase-producing (87.5%, 7/8). The β-lactamase-negative strains were verified through borderline experiments. Ticlosanin, vancomycin, linezolid and daptomycin demonstrated 100% sensitivity in all eight cases of S. aureus. Of the 12 strains of \u003cem\u003eenterococci\u003c/em\u003e, six were of the \u003cem\u003eEnterococcus faecalis\u003c/em\u003e variety and six were of the \u003cem\u003eEnterococcus faecalis\u003c/em\u003e variety. Additionally, three strains of vancomycin-resistant enterococci (VRE) were identified, representing 25% of the total number of enterococci strains (3/12). A significant proportion of the \u003cem\u003eEnterococcus faecalis\u003c/em\u003e isolates were vancomycin-resistant, with a prevalence of 50%. All three strains of vancomycin-resistant Enterococcus (VRE) exhibited resistance to ticlosanin and sensitivity to linezolid. Four cases of high-level aminoglycoside resistance were identified, comprising three cases of E. faecalis and one case of E. faecalis. The streptococci exhibited bradyzoites, demonstrating sensitivity to penicillin G, vancomycin, linezolid, ceftriaxone, ceftizoxime, meropenem, tigecycline, and levofloxacin. The three cases of Candida were identified as \u003cem\u003eCandida albicans\u003c/em\u003e, \u003cem\u003eCandida smoothii\u003c/em\u003e, and \u003cem\u003eCandida tropicalis\u003c/em\u003e. All three strains of the fungus demonstrated susceptibility to fluconazole (minimum inhibitory concentration (MIC)\u0026thinsp;\u0026le;\u0026thinsp;1 mg/L), as per the Clinical and Laboratory Standards Institute (CLSI) guidelines.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eGlobally, bloodstream infections (BSIs) are the leading cause of morbidity and mortality in critically ill patients. Blood culture remains the gold standard for laboratory diagnosis of BSIs\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. It is worth noting that there are some unavoidable problems with blood culture, and relevant studies have shown that the positive rate of blood culture is only about 70%\u003csup\u003e25\u003c/sup\u003e, and the volume of blood collected is a key factor in the recovery of pathogens from blood culture\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, while the volume of blood inoculated also determines the sensitivity and specificity of blood culture.For adults, the recommended blood volume is about 38\u0026ndash;40 ml/times of blood culture from both sides of the double set of blood culture, including aerobic and anaerobic bottles, in order to improve the sensitivity of blood culture and identify clinical contamination. It is generally believed that the positive rate decreases by 1% for every 1 ml decrease in the volume of blood, and the high demand for blood volume brings great trouble and low compliance to the clinical delivery of the test\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. In addition, the reporting time of positive specimens is related to the bacterial concentration and strain in the blood, generally Gram-positive cocci and fungi take longer time to report positive than Gram-negative bacilli, and it takes at least 6\u0026ndash;8 hours to culture colonies after reporting positive to identify strain type, and it takes longer for fungi and Gram-positive cocci, and the cycle of blood culture is generally 5\u0026ndash;7 d. Even if the samples are collected in full compliance with the operation procedure, it is still difficult to avoid the contamination problem. Even when samples are collected in full accordance with the protocols, it is difficult to avoid contamination problems, and there is still a clinical contamination rate of 1\u0026ndash;3%\u003csup\u003e27\u003c/sup\u003e. This creates an urgent need for timely and sensitive new methods to be applied in the diagnosis and differential diagnosis of bloodstream infections. Several studies have validated the accuracy and sensitivity of ddPCR in the diagnosis of infection-related diseases\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.To overcome the drawbacks of blood culture in the diagnosis of BSI, we developed a culture-independent ddPCR method for the rapid and accurate identification of the four most common pathogens, including Staphylococcus aureus, Enterococcus, Streptococcus, and Candida, in blood samples from patients with suspected BSI. only 3 ml of anticoagulant blood is required in the ddPCR assay to isolate nucleic acids for detection, which greatly reduces the volume of blood collection and improves the quality of blood bacteria. This greatly reduces the amount of blood collection and improves compliance with blood bacterial testing. In addition, ddPCR can quickly and promptly issue a preliminary report for the clinic, with a negative predictive value of up to 100% within 4 hours, which suggests that clinically suspected bloodstream infections can be ruled out within 4 hours, providing a preliminary diagnosis of bloodstream infections for the clinic.\u003c/p\u003e \u003cp\u003eIn addition, studies have shown that for some critically ill patients with BSIs their signs and symptoms are usually non-specific, which poses a great challenge for the diagnosis and treatment of clinical BSIs, and it has been shown that about 35.0% of sepsis patients are culture-negative\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and the mortality rate of these BC-negative patients is significantly higher than that of regular patients. Therefore, there remains a need for other appropriate diagnostic methods to complement conventional BC to identify possible pathogens in BC-negative sepsis patients. In our study, ddPCR identified 14 patients with negative blood cultures along with 4 patients with dual infections compared to conventional blood culture methods, and the mortality and 28-day readmission rates of ddPCR-positive patients were not significantly different from those of BC-positive patients, suggesting that ddPCR\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, as a complementary method to conventional blood cultures, may have higher sensitivity to identify possible BC-negative septicaemic patients, providing a basis for clinical treatment and more timely prediction of patient outcome. Relevant studies have shown that ddPCR technology as a novel, rapid, absolute quantitative nucleic acid detection technology has been applied in many aspects, for example, some studies have shown that ddPCR can be used for tumour-related gene mutations and post-treatment monitoring of tumours, and other relevant studies have shown that ddPCR has also been used in the detection of SARS, hepatitis viruses, and HIV viruses, and the absolute quantification of their infections can be explored by ddPCR. In addition, studies have shown that ddPCR has also been used in the detection of SARS, hepatitis viruses, HIV viruses, to investigate the severity of the infection and the healing of patients through ddPCR, and ddPCR can also play a very good role in the detection of some difficult to culture some of the pathogenic bacteria or parasites, such as Mycobacterium tuberculosis, Shreiman protozoa, Plasmodium falciparum, and so on. In addition, further studies have shown that the dynamic detection of the absolute value of the ddPCR copy of the long term, with the PCT and CRP white blood cell counts and so on, it was found that the BSI related ddPCR. The dynamic absolute value counts of ddPCR may have some correlation with the severity of inflammatory infections. ddPCR even provides accurate and quantitative pathogen load data in time, partly reflecting the severity of infection and inflammation Pathogen load is often closely related to the severity of BSI.\u003c/p\u003e \u003cp\u003eThe human immune system and antibiotic therapy kill invaded pathogens in BSIs, resulting in the release of nucleic acids from the pathogen into the bloodstream; this nucleic acid is thought to be part of circulating cfDNA. ddPCR assists in the diagnosis of sepsis by detecting free DNA in the bloodstream to assist in determining the presence or absence of free pathogens in the bloodstream of patients\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Our results showed that ddPCR could identify S. aureus, enterococci, streptococci and Candida in whole blood samples within 4 h. The sensitivity for each strain was 100%, and the specificity was above 90%, in addition to the lower line of detection for positive coccobacilli and Candida was 0.05 and 0.1 copies/\u0026micro;l, respectively. ddPCR not only identifies the blood culture based test patients who are positive, studies have shown that ddPCR has a positive rate of 30\u0026ndash;50% for bloodstream infections compared to the 10\u0026ndash;12% positive rate of traditional BC methods. However, it is worth noting that this is a limitation of this study and does not allow for the diagnosis of BSI by ddPCR positivity. firstly, as microbial DNA may be released from dead pathogens and can still detect pathogen nucleic acids two weeks after a negative report from routine blood cultures, the presence of microbial DNA in the blood does not necessarily indicate that there is a live microorganism in the blood stream, but may also However, it is undeniable that ddPCR does have higher sensitivity and specificity in negative prediction and positive specimen turnover than conventional BC methods. In addition, we found two methicillin-resistant Staphylococcus aureus strains in BC-positive S. aureus and two vancomycin-resistant enterococci in enterococci, which is of great significance for clinical treatment, but the ddPCR method was not involved in the present experiment, and some studies have shown that the drug-resistant characteristics of S. aureus can be preliminarily determined by detecting related genes such as the MecA/C genes\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, which can provide guidance on drug use in the clinic. Some studies have shown that ddPCR can be used to preliminarily determine the drug resistance characteristics by detecting related genes such as MecA/C gene, which can provide clinical guidance for drug use\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In conclusion, ddPCR still cannot completely replace traditional blood culture, but due to its high sensitivity and specificity, it can be an important supplement to traditional blood culture for diagnosis and exclusion of sepsis.\u003c/p\u003e \u003cp\u003eIn conclusion, we have successfully developed a new ddPCR method to detect four major pathogens in patients with suspected BSI, and clinical validation showed that our method is higher than blood cultures in terms of specificity, sensitivity, and turnaround time, making it a promising method for early and accurate diagnosis of BSI. However, there are several limitations of this study that need to be mentioned, in this preliminary study we only evaluated the four categories of pathogens included, Gram-negative bacilli and viruses were not involved, and secondly, ddPCR, as a molecular method, can only detect nucleic acid fragments in the blood, and is not able to judge to determine whether it is a true bloodstream infection, so there is a need to add a variety of assays such as blood cultures and serological tests such as PCT, and the diagnosis of blood stream infections is complex and multiple assays are required. The diagnosis of bloodstream infections is complex, and the combined use of multiple testing methods can lead to a more rapid and accurate diagnosis of BSI and make the results more meaningful for clinical diagnosis and treatment.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur team has pioneered the use of multiplex ddPCR to test blood samples for four types of pathogens suspected to be BSIs. Clinical validation has demonstrated that our method outperforms blood cultures in terms of specificity, sensitivity, and turnaround time. ddPCR holds the potential to serve as a supplementary tool to traditional blood culture methods in the near future for diagnosing bloodstream infections. It can enhance the diagnostic process by providing timely and sensitive assistance in the diagnosis and differential diagnosis of bloodstream infections.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBSI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ebloodstream infection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eddPCR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edigital polymerase chain reaction assay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWBC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhite blood cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNEU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeutrophil\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLYM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLymphocyte\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEOS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEosinophils\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIQR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePCT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProcalcitonin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePPV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epositive predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNPV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enegative predictive value\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the inclusion of human subjects was granted by the Ethics Committee of The First Affiliated Hospital of Guangzhou Medical University, with approval codes 2022 No.121 and 2024 No. G-007. The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared that no competing interest exists.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Chinese National Natural Science Foundation (81960023), National Key Research and Development Program of China (2023YFF1203800), Natural Science Foundation of Guangdong Province (2023A1515012917), Natural Science Foundation of Guangdong Province (2024A1515012830), State Key Laboratory Project (SKLRD-Z-202305), Research enhancement project of Guangzhou medical university (No.2024SRP074). Major clinical research project of Guangzhou Medical University (GMUCR2024-02009)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiuyong You, Shuang Liu, Zhiwei Lin should be regarded as the first author. Conception and design of the research: Baoqing Sun, Yueting Jiang; Drafting the manuscript: Liuyong You, Shuang Liu. Article structure design: Shuang Liu, Zhiwei Lin; Statistical analysis: Liuyong You, Fengrong Chen, Ru Wang. Samples collection and detection: Liuyong You, Xiaojun Ou, Donghua Feng; Acquisition of data: Liuyong You, Xiaojun Ou; Experimental management: Liuyong You, Donghua Feng. All authors read and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the laboratory Department of the First Affiliated Hospital of Guangzhou Medical University for providing the experimental environment.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDunbar, S. A.; Gardner, C.; Das, S. Diagnosis and Management of Bloodstream Infections With Rapid, Multiplexed Molecular Assays. \u003cem\u003eFrontiers in Cellular and Infection Microbiology \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e12\u003c/em\u003e. DOI: 10.3389/fcimb.2022.859935.\u003c/li\u003e\n\u003cli\u003eJohn F, M.; Elda, R.; Hugh, W.; Gunter F, H.; Patrick N A, H.; David L, P. Long-term morbidity and mortality following bloodstream infection: A systematic literature review. \u003cem\u003eJ Infect \u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e, \u003cem\u003e77\u003c/em\u003e (1). DOI: 10.1016/j.jinf.2018.03.005.\u003c/li\u003e\n\u003cli\u003eRicard, F.; Ignacio, M.-L.; Gary, P.; Tiffany M, O.; Sean, T.; R Phillip, D.; Antonio, A.; Christa, S.; Mitchell M, L. Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: results from a guideline-based performance improvement program. \u003cem\u003eCrit Care Med \u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e42\u003c/em\u003e (8). DOI: 10.1097/ccm.0000000000000330.\u003c/li\u003e\n\u003cli\u003eAnand, K.; Daniel, R.; Kenneth E, W.; Bruce, L.; Joseph E, P.; Satendra, S.; Robert, S.; Daniel, F.; Sergio, Z.; Leo, T.; et al. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. \u003cem\u003eCrit Care Med 34\u003c/em\u003e (6).\u003c/li\u003e\n\u003cli\u003eStephanie S, B.; Bereneice, M.; Susan R, S.; James H, D.; Nancy E, C.; Michael A, S.; Alice S, W.; Melvin P, W.; Edward B, L.; Donna M, W. Effectiveness of Practices To Increase Timeliness of Providing Targeted Therapy for Inpatients with Bloodstream Infections: a Laboratory Medicine Best Practices Systematic Review and Meta-analysis. \u003cem\u003eClin Microbiol Rev \u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e29\u003c/em\u003e (1). DOI: 10.1128/cmr.00053-14.\u003c/li\u003e\n\u003cli\u003eVincent X, L.; Vikram, F.-S.; John D, G.; Jennifer M, B.; Theodore J, I.; Jay, B.; Gabriel J, E. The Timing of Early Antibiotics and Hospital Mortality in Sepsis. \u003cem\u003eAm J Respir Crit Care Med \u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e196\u003c/em\u003e (7). DOI: 10.1164/rccm.201609-1848OC.\u003c/li\u003e\n\u003cli\u003eTimsit, J.; Rupp\u0026eacute;, E.; Barbier, F.; Tabah, A.; Bassetti, M. Bloodstream infections in critically ill patients: an expert statement. \u003cem\u003eIntensive care medicine \u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e46\u003c/em\u003e (2), 266-284. DOI: 10.1007/s00134-020-05950-6.\u003c/li\u003e\n\u003cli\u003eCheng, M.; Stenstrom, R.; Paquette, K.; Stabler, S.; Akhter, M.; Davidson, A.; Gavric, M.; Lawandi, A.; Jinah, R.; Saeed, Z.; et al. Blood Culture Results Before and After Antimicrobial Administration in Patients With Severe Manifestations of Sepsis: A Diagnostic Study. \u003cem\u003eAnnals of internal medicine \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e171\u003c/em\u003e (8), 547-554. DOI: 10.7326/m19-1696.\u003c/li\u003e\n\u003cli\u003eJianfeng, X.; Hongliang, W.; Yan, K.; Lixin, Z.; Zhongmin, L.; Bingyu, Q.; Xiaochun, M.; Xiangyuan, C.; Dechang, C.; Weihua, L.; et al. The Epidemiology of Sepsis in Chinese ICUs: A National Cross-Sectional Survey. \u003cem\u003eCrit Care Med \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e48\u003c/em\u003e (3). DOI: 10.1097/ccm.0000000000004155.\u003c/li\u003e\n\u003cli\u003eVincent, J.; Sakr, Y.; Singer, M.; Martin-Loeches, I.; Machado, F.; Marshall, J.; Finfer, S.; Pelosi, P.; Brazzi, L.; Aditianingsih, D.; et al. Prevalence and Outcomes of Infection Among Patients in Intensive Care Units in 2017. \u003cem\u003eJAMA \u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e323\u003c/em\u003e (15), 1478-1487. DOI: 10.1001/jama.2020.2717.\u003c/li\u003e\n\u003cli\u003eAnthony D, B.; Carson K L, L.; Adam S, K.; Mallika, S.; Kevin, G.; Akhil, G.; Pranav, T.; Julien, S.; Olivier, D. C.; Isabella, S.; et al. Staphylococcus aureus bacteraemia mortality: a systematic review and meta-analysis. \u003cem\u003eClin Microbiol Infect \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e28\u003c/em\u003e (8). DOI: 10.1016/j.cmi.2022.03.015.\u003c/li\u003e\n\u003cli\u003eAthena P, K.; Kelly, H.; James, B.; Yi, M.; Isaac, S.; Erin, E.; Joelle, N.; Marion A, K.; Ghinwa, D.; Susan, P.; et al. Vital Signs: Epidemiology and Recent Trends in Methicillin-Resistant and in Methicillin-Susceptible Staphylococcus aureus Bloodstream Infections - United States. \u003cem\u003eMMWR Morb Mortal Wkly Rep \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e68\u003c/em\u003e (9). DOI: 10.15585/mmwr.mm6809e1.\u003c/li\u003e\n\u003cli\u003eSe\u0026aacute;na, D.; Ines, L.; Kerstin, H.; Oliver, K. Host response to Candida albicans bloodstream infection and sepsis. \u003cem\u003eVirulence \u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e6\u003c/em\u003e (4). DOI: 10.4161/21505594.2014.988096.\u003c/li\u003e\n\u003cli\u003eCairns, K.; Udy, A.; Peel, T.; Abbott, I.; Dooley, M.; Peleg, A. Therapeutics for Vancomycin-Resistant Enterococcal Bloodstream Infections. \u003cem\u003eClinical microbiology reviews \u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e36\u003c/em\u003e (2), e0005922. DOI: 10.1128/cmr.00059-22.\u003c/li\u003e\n\u003cli\u003eBai, A.; Lo, C.; Komorowski, A.; Suresh, M.; Guo, K.; Garg, A.; Tandon, P.; Senecal, J.; Del Corpo, O.; Stefanova, I.; et al. Staphylococcus aureus bacteraemia mortality: a systematic review and meta-analysis. \u003cem\u003eClinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e28\u003c/em\u003e (8), 1076-1084. DOI: 10.1016/j.cmi.2022.03.015.\u003c/li\u003e\n\u003cli\u003eDuggan, S.; Leonhardt, I.; H\u0026uuml;nniger, K.; Kurzai, O. Host response to Candida albicans bloodstream infection and sepsis. \u003cem\u003eVirulence \u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e6\u003c/em\u003e (4), 316-326. DOI: 10.4161/21505594.2014.988096.\u003c/li\u003e\n\u003cli\u003eStephen J, S.; Keith R, J. Digital PCR-An Emerging Technology with Broad Applications in Microbiology. \u003cem\u003eClin Chem \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e66\u003c/em\u003e (1). DOI: 10.1373/clinchem.2019.304048.\u003c/li\u003e\n\u003cli\u003eDanrui, W.; Shang, W.; Xiongfeng, D.; Qing, H.; Yue, L.; Zhujun, W.; Kai, F.; Yan, L.; Ye, D. ddPCR surpasses classical qPCR technology in quantitating bacteria and fungi in the environment. \u003cem\u003eMol Ecol Resour \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e22\u003c/em\u003e (7). DOI: 10.1111/1755-0998.13644.\u003c/li\u003e\n\u003cli\u003eDominic J, V.; Dhavan, S.; Amy, W.; Larissa, M.; Madison, C.; Lauren M, J.; John, A.; Kevin, D.; Arlene, D. S.; Grace, S.; et al. Mutant KRAS in Circulating Tumor DNA as a Biomarker in Localized Pancreatic Cancer in Patients Treated with Neoadjuvant Chemotherapy. \u003cem\u003eAnn Surg \u003c/em\u003e\u003cstrong\u003e2024\u003c/strong\u003e, (0). DOI: 10.1097/sla.0000000000006562.\u003c/li\u003e\n\u003cli\u003eLi, Y.; Huang, K.; Yin, J.; Tan, Z.; Zhou, M.; Dai, J.; Yi, B. Clinical evaluation of a multiplex droplet digital PCR for pathogen detection in critically ill COVID-19 patients with bloodstream infections. \u003cem\u003eInfection \u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e52\u003c/em\u003e (3), 1027-1039. DOI: 10.1007/s15010-023-02157-x.\u003c/li\u003e\n\u003cli\u003eXiaocui, W.; Guangkun, T.; Chunlei, S.; Yang, W.; Jinghui, Y.; Chunqiu, W.; Chaohui, H.; Fangyou, Y. Targeted next-generation sequencing - a promising approach in the diagnosis of Mycobacterium tuberculosis and drug resistance. \u003cem\u003eInfection \u003c/em\u003e\u003cstrong\u003e2024\u003c/strong\u003e, (0). DOI: 10.1007/s15010-024-02411-w.\u003c/li\u003e\n\u003cli\u003eShao, Z.; Zhu, J.; Wei, Y.; Jin, J.; Zheng, Y.; Liu, J.; Zhang, R.; Sun, R.; Hu, B. Pathogen load and species monitored by droplet digital PCR in patients with bloodstream infections: A prospective case series study. \u003cem\u003eBMC Infectious Diseases \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e22\u003c/em\u003e (1). DOI: 10.1186/s12879-022-07751-2.\u003c/li\u003e\n\u003cli\u003eIngrid, Z.; Sara, C.; Gunl\u0026ouml;g, R.; Theresa, E.; Paula, M.; Kristoffer, S. High nuc DNA load in whole blood is associated with sepsis, mortality and immune dysregulation in Staphylococcus aureus bacteraemia. \u003cem\u003eInfect Dis (Lond) \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e51\u003c/em\u003e (3). DOI: 10.1080/23744235.2018.1562205.\u003c/li\u003e\n\u003cli\u003eW V, K.; S, R. Burden of bacterial bloodstream infection-a brief update on epidemiology and significance of multidrug-resistant pathogens. \u003cem\u003eClin Microbiol Infect \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e26\u003c/em\u003e (2). DOI: 10.1016/j.cmi.2019.10.031.\u003c/li\u003e\n\u003cli\u003eTowns, M.; Jarvis, W.; Hsueh, P. Guidelines on blood cultures. \u003cem\u003eJournal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi \u003c/em\u003e\u003cstrong\u003e2010\u003c/strong\u003e, \u003cem\u003e43\u003c/em\u003e (4), 347-349. DOI: 10.1016/s1684-1182(10)60054-0.\u003c/li\u003e\n\u003cli\u003eBae, M.; In Kim, H.; Park, J.; Ryu, B.; Chang, J.; Sung, H.; Jung, J.; Kim, M.; Kim, S.; Lee, S.; et al. Improvement of blood culture contamination rate, blood volume, and true positive rate after introducing a dedicated phlebotomy team. \u003cem\u003eEuropean journal of clinical microbiology \u0026amp; infectious diseases : official publication of the European Society of Clinical Microbiology \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e38\u003c/em\u003e (2), 325-330. DOI: 10.1007/s10096-018-3430-4.\u003c/li\u003e\n\u003cli\u003eDoern, G.; Carroll, K.; Diekema, D.; Garey, K.; Rupp, M.; Weinstein, M.; Sexton, D. Practical Guidance for Clinical Microbiology Laboratories: A Comprehensive Update on the Problem of Blood Culture Contamination and a Discussion of Methods for Addressing the Problem. \u003cem\u003eClinical microbiology reviews \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e33\u003c/em\u003e (1). DOI: 10.1128/cmr.00009-19.\u003c/li\u003e\n\u003cli\u003eYannick, W.; Daisy, D.; Anke, C.; Hennie M J, R.; Heiman F, W.; Chantal P, B.-R.; Ren\u0026eacute; H, T. M.; Geert J A, W. Droplet digital polymerase chain reaction for rapid broad-spectrum detection of bloodstream infections. \u003cem\u003eMicrob Biotechnol \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e13\u003c/em\u003e (3). DOI: 10.1111/1751-7915.13491.\u003c/li\u003e\n\u003cli\u003eGupta, S.; Sakhuja, A.; Kumar, G.; McGrath, E.; Nanchal, R.; Kashani, K. Culture-Negative Severe Sepsis: Nationwide Trends and Outcomes. \u003cem\u003eChest \u003c/em\u003e\u003cstrong\u003e2016\u003c/strong\u003e, \u003cem\u003e150\u003c/em\u003e (6), 1251-1259. DOI: 10.1016/j.chest.2016.08.1460.\u003c/li\u003e\n\u003cli\u003eIngrid, Z.; Per, J.; Per, O.; Paula, M.; Kristoffer, S. Quantitative data from the SeptiFast real-time PCR is associated with disease severity in patients with sepsis. \u003cem\u003eBMC Infect Dis \u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e14\u003c/em\u003e (0). DOI: 10.1186/1471-2334-14-155.\u003c/li\u003e\n\u003cli\u003eZiqiang, S.; Jingwen, Z.; Yanyan, W.; Jun, J.; Yang, Z.; Jingquan, L.; Run, Z.; Renhua, S.; Bangchuan, H. Pathogen load and species monitored by droplet digital PCR in patients with bloodstream infections: A prospective case series study. \u003cem\u003eBMC Infect Dis \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e22\u003c/em\u003e (1). DOI: 10.1186/s12879-022-07751-2.\u003c/li\u003e\n\u003cli\u003eEmily M, E.; Christiaan R, d. V.; Felicia, R.; Batu, S.-K.; Lawrence, P.; David, H.; Erick R, S.; Lily, B.; Nicholas, D.; Desiree H, H.; et al. Microbial Cell-Free DNA Identifies Etiology of Bloodstream Infections, Persists Longer Than Conventional Blood Cultures, and Its Duration of Detection Is Associated With Metastatic Infection in Patients With Staphylococcus aureus and Gram-Negative Bacteremia. \u003cem\u003eClin Infect Dis \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e74\u003c/em\u003e (11). DOI: 10.1093/cid/ciab742.\u003c/li\u003e\n\u003cli\u003eCarlo, G.; Liselotte Diaz, H.; Hanna, B.; Tim, E.; Christian G, G.; Ole E, H.; Vincent, J.; Gunnar, K.; Danilo, L. F. W.; Jos, M.; et al. Staphylococcus aureus bloodstream infections: diverging trends of meticillin-resistant and meticillin-susceptible isolates, EU/EEA, 2005 to 2018. \u003cem\u003eEuro Surveill \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e26\u003c/em\u003e (46). DOI: 10.2807/1560-7917.Es.2021.26.46.2002094.\u003c/li\u003e\n\u003cli\u003eWu, J.; Tang, B.; Qiu, Y.; Tan, R.; Liu, J.; Xia, J.; Zhang, J.; Huang, J.; Qu, J.; Sun, J.; et al. Clinical validation of a multiplex droplet digital PCR for diagnosing suspected bloodstream infections in ICU practice: a promising diagnostic tool. \u003cem\u003eCritical Care \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e26\u003c/em\u003e (1). DOI: 10.1186/s13054-022-04116-8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bloodstream infection (BSI), droplet digital polymerase chain reaction (ddPCR), sepsis, pathogen detection, rapid diagnostics","lastPublishedDoi":"10.21203/rs.3.rs-6320844/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6320844/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe current gold standard for the detection of bloodstream infections has several limitations, including a low positivity rate, a high contamination rate, and a lengthy flow cycle. Droplet digital polymerase chain reaction (ddPCR), a novel technique offering high sensitivity and specificity and the ability to provide absolute quantification, has not been extensively investigated in the context of bloodstream infections.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective study was conducted on 77 patients with suspected bloodstream infections, with blood cultures and ddPCR used to investigate the presence of four pathogens. Additionally, the general characteristics of the patients and relevant routine tests were collected and analysed to assess the diagnostic efficacy of ddPCR for bloodstream infections.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e77 patients from intensive care medicine, respiratory medicine, and other departments considering BSIs were enrolled. Blood cultures identified 24 positive samples, including 8 Staphylococcus aureus, 12 Enterococcus species, 1 Streptococcus species, and 3 Candida species.. The ddPCR assay demonstrated 100% sensitivity for all four pathogen categories and specificities of 94.20%, 92.31%, 93.42%, and 94.59%, respectively. In addition, the ddPCR assay simultaneously detected 4 cases of polymicrobial infections and 14 cases of negative blood cultures, which was verified by Sanger sequencing. The ddPCR assay's detection time was significantly shorter than that of blood cultures, with an average of 4.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94 hours compared to 68.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.50 hours (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe present study demonstrates that droplet digital polymerase chain reaction (ddPCR) significantly reduces the turnaround time of positive blood culture specimens. Furthermore, it has high sensitivity, specificity and negative predictive value, and enables early diagnosis of bloodstream infections. A larger number of samples is required to validate the correlation between absolute ddPCR quantification and patient severity and prognosis.\u003c/p\u003e","manuscriptTitle":"Application of ddPCR in the rapid diagnosis of bloodstream infections: detection of Staphylococcus, Enterococcus spp, Streptococcus spp and Candida spp","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 13:21:40","doi":"10.21203/rs.3.rs-6320844/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6a17ce34-d609-4e88-b0f1-e0e5e2a4e982","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-06T13:21:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 13:21:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6320844","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6320844","identity":"rs-6320844","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}