Application value of ddPCR in rapid detection of pathogens in abdominal sepsis

Patient enrollment

In this prospective study, we conducted an initial sample size calculation with a statistical power of 0.8 and a significance level (α) of 0.05. Subsequently, we enrolled patients diagnosed with abdominal sepsis in the Department of Intensive Care Medicine at the First Affiliated Hospital of Chongqing Medical University from January 2021 to December 2022. The study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (N0.2022-K132).

Abdominal sepsis was defined as infection with a source in the abdominal cavity meeting the Sepsis-3 diagnostic criteria published jointly by the Society of Critical Care Medicine (SCCM) and the European Society for Intensive Care Medicine [6].

The following exclusion criteria were applied: patients without appropriate abdominal effusion for bacterial culture and ddPCR; patients<18 years old or>95 years old; patients expected to die within 24 h; patients in hospital for more than seven days due to abdominal infection prior to their ICU admission; patients diagnosed with hospital-acquired infection prior to their ICU admission; and patients without a signed informed consent form.

Data collection

The clinical data of the enrolled participants included demographic data such as gender and age, as well as previous medical history, APACHE II score, and SOFA score based on laboratory tests performed in ICU. Abdominal fluid was collected via abdominal a drainage tube, and sampling was performed for bacterial Gram staining, bacterial isolation by plate streaking, culture in serum-agar plate medium at 37 °C, drug sensitivity detection, and ddPCR. We employed 28-day survival as the endpoint of this study, data of the ICU stay of the patients were also collected.

DNA extraction and ddPCR testing

The following procedures were implemented for sample preparation. The abdominal fluid was shaken for proper mixing. Then, a 5 mL sample was collected into a 10/15 mL centrifuge tube, followed by centrifugation at 3,000 rpm for 10 min. Further, the supernatant was discarded to the remaining 1 mL, and the remaining supernatant and precipitates were transferred into a 1.5 mL centrifuge tube for voracious mixing. Next, 200 μL samples were collected and transferred into 1.5 mL tubes. Then, 800 μL of digestive fluid was added, and the samples were incubated at 56° for 10 min. Following incubation, centrifuge at 1,000 rpm for 5 min to collect the supernatant. After collecting the supernatant, add an appropriate amount of Auto-Pure 32A automated magnetic bead nucleic acid extraction solution (Hangzhou Ao Sheng, Hangzhou, China), shake thoroughly, and incubate at 37 °C for 10 min. Subsequently, place the mixture on the magnetic stand of the Auto-Pure 32A, add 300 μL of washing solution, and perform two washes. Finally, add 50 μL of TE buffer for elution. Retain 2 mL of the DNA supernatant and store it in a −20 °C freezer for subsequent ddPCR analysis.

According to the study by Wu et al. [5], the 2019 Chinese Guidelines for the Diagnosis and Treatment of Intra-abdominal Infections, along with the results of our hospital’s previous pathogen surveillance and antibiotic susceptibility testing, led to the design of a specific multiplex ddPCR identification panel. This panel is intended to identify five Gram-negative bacteria (Escherichia coli, Enterobacter cloacae, Klebsiella, Pseudomonas aeruginosa, Acinetobacter baumannii complex), four Gram-positive bacteria (Enterococcus, Streptococcus, Staphylococcus aureus, Coagulase-negative Staphylococcus), one fungus (Candidiins), and four resistance genes (blaKPC, VanA + VanM, mecA, Oxa-48). Subsequently, the Pilot Gene Droplet Digital PCR System, a five-channel fluorescent ddPCR system (Pilot Gene Technology Company, Hangzhou, China), was employed to detect pathogens and resistance genes according to the manufacturer’s protocol (Sangon Biotech, Shanghai). In accordance with the manufacturer’s recommendations, a ddPCR reaction mixture with a final volume of 20 μL was prepared, consisting of 2.0 μL DNA, 1 × ddPCR probe supermix (BioRad, City, USA), 800 nM specific primers, 250 nM probes, and DNase-free water. The mixture was then transferred to a droplet generator (AD16, Pilot Gene Tech.) to produce approximately 20,000 water-in-oil emulsion droplets within the chip. These droplets were subsequently loaded into a thermal cycler (Bio-Rad) for amplification, which involved an initial denaturation at 95 °C for 10 min, followed by 40 cycles of denaturation at 95 °C for 30 s, annealing/extension at 62 °C for 1 min, and enzyme inactivation at 98 °C for 10 min, concluding with a stabilization step at 12 °C for 30 min. After amplification, the droplets were measured using a reader, and the data were analyzed with Bio-Rad Quanta Soft Analysis Pro software. Based on previous test data from our institution, a copy number greater than 45 copies/mL is defined as positive.

Statistical analysis

Statistical analysis was performed using SPSS 26.0 and GraphPad Prism 8.0.1 software. Figures were created by RStudio 2022-09-06 and Excel 2019. Continuous variables were tested by S-W test for data distribution. Continuous variables with normal distribution were expressed as mean ± standard deviation, and continuous variables without normal distribution were expressed as median and inter-quartile range. Categorical variables were represented as count and percentage. Student’s t-test was applied for comparison of continuous variables with normal distribution, and Mann-Whitney U-test was implemented for comparison of continuous variables without normal distribution. Chi-square test was employed for comparison of categorical variables. A 2 × 2 contingency table was established to determine the positive percent agreement (PPA), negative percent agreement (NPA), positive predictive value (PPV), and negative predictive value (NPV). All statistical analyses were performed at an absolute value of 95 % confidence intervals (95 % CI). Chi-square or Fisher’s exact test were used for the PPA comparison as unpaired between-group samples.

Clinical characteristics of the included 164 patients

A total number of 205 patients with abdominal sepsis were admitted during the study, including one patient with inappropriate abdominal drainage bacterial culture samples, seven patients with age not meeting the inclusion criteria, five patients in hospital more than seven days due to abdominal infection prior to ICU, six patients without informed consent, and 22 patients with insufficient clinical data (19 patients with incomplete laboratory data and three patients with unknown clinical outcomes). Eventually, 164 patients were enrolled in this study (Figure 1). A total number of 164 ddPCR samples, 228 drainage smear samples, and 490 (245*2) drainage culture samples were collected from these patients.

Figure 1:

Patient selection flowchart.

The mean age of the 164 patients, 82 males (50 %) and 82 females (50 %), with abdominal septic shock was 69.7 (24.091.0) years. Of them, 69 (42.1 %) patients were with history of cardiovascular disease, 27 (16.5 %) with diabetes, and 19 (11.6 %) with chronic obstructive pulmonary disease. Gastrointestinal perforation, cholangitis, and abdominal infection after surgery were the main causes of septic shock accounting for 18.3 % (30), 34.8 % (57), and 47.0 % (77) cases, respectively. Fifty-one (31.1 %) patients developed shock and received vasopressors during ICU, and 26 (15.9 %) patients died within 28 days (Table 1).

Distribution of pathogens and the infection types in the patients

Pathogen detection results in ddPCR

ddPCR was performed using drainage fluid from 164 patients with abdominal sepsis. Of them, 141 patients were ddPCR-positive, with a positive detection rate of 86 % (141/164, 95% CI of 79.70–90.9 %). Bacteria, including 58.7 % (122/208) Gram-negative bacteria and 41.3 % (86/208) Gram-positive bacteria, were detected in 208 samples collected from the 164 patients (Figure 2A). Most samples with Gram-negative bacteria contained Escherichia coli (n=59), followed by Enterobacter cloacae (n=20), Klebsiella (n=20), Pseudomonas aeruginosa (n=18), and Acinetobacter baumannii complex (n=5). The predominant part of the Gram-positive bacteria was constituted by Enterococcus (n=61), Streptococcus (n=13), Staphylococcus aureus (n=8), and coagulase-negative Staphylococcus (coNS) (n=4). Candidinoid fungi were found in 36 cases (Figure 2B). We found that the samples from 55.3 % (78/141) of the patients had multiple bacterial genera, including 56 patients with two bacterial genera (n=56), 19 patients with three bacterial genera (n=19), and three patients with four bacterial genera (Figure 2C).

Figure 2:

Distribution of pathogens and the infection types in the patients.

Comparison of ddPCR and routine detection

Comparison of ddPCR and routine drainage fluid culture

A number of 111 cases (111/164=67.7 %) were positive for both ddPCR and routine drainage fluid culture, and 17 cases (17/164=10.4 %) were negative for both ddPCR and routine drainage fluid culture. Additionally, 30 cases (30/164=18.3 %) were positive for pathogens detected only by ddPCR and 6 cases (6/164=3.7 %) were positive for pathogens detected only by routine drainage fluid culture (Table 2A).

Table 2:

Consistency between ddPCR and routine tests (A: consistency between ddPCR and drainage fluid culture, B: consistency between ddPCR and drainage fluid smear).

2A
Pathogen	ddPCR (+)/culture (−)	ddPCR (+)/culture (+)	ddPCR (−)/culture (+)	ddPCR (−)/culture (−)	PPA, %	NPA, %	PPV, %	PPV, %
All pathogens	30	111	6	17	94.87	36.17	78.72	73.91
Bacteria	8	109	30	17	93.16	36.17	78.42	68.00
Gram negative bacteria	25	97	6	36	94.17	59.02	79.51	85.71
Escherichia colie	16	43	3	102	93.48	86.44	72.88	97.14
Gram positive bacteria	19	67	12	66	84.81	77.65	77 91	84.62
Enterococcus	18	41	5	100	89.13	84.75	69.49	95.24
Fungi	22	14	1	127	93.33	85.23	38.80	99.22
A pathogen Multiple	4	59	5	96	92.19	96.00	93.65	95.05
pathogens	2	61	3	98	95.31	98.00	96.83	97.03
2B
Pathogens	ddPCR (+)/smear (−)	ddPCR (+)/smear (+)	ddPCR (−)/smear (+)	ddPCR (−)/smear (−)	PPA, %	NPA, %	PPV, %	NPV, %
All pathogens	20	121	8	35	93.80	42.86	85.82	65.22

Of the 111 double-positive patients, ddPCR matched routine drainage fluid culture perfectly in 75 cases (strains by ddPCR matched strains by routine drainage fluid culture perfectly). Partially matching was observed in 25 cases (at least one pathogen overlapped between ddPCR and drainage fluid culture), and complete mismatching was found in 11 cases. Of the 25 perfectly matched cases, only seven had mixed infection, whereas the other 18 cases had single-pathogen infection. Out of the total 164 cases, ddPCR identified nine cases with two pathogens (5 cases of Acinetobacter baumannii complex and 4 cases of coNS) that were not positive in the routine drainage fluid culture (Figures 3 and 4).

Figure 3:

Distribution of pathogens.

Figure 4:

Consistent distribution of pathogens.

In the detection of specific pathogens, the specificity of ddPCR technology for fungal is significantly higher than bacteria. Furthermore, ddPCR exhibits greater sensitivity in detecting Gram-negative bacteria, albeit with relatively lower specificity. Notably, ddPCR demonstrates superior sensitivity and specificity in detecting Escherichia coli compared to Enterococcus (Table 2A).

Comparison of the reporting time among ddPCR, abdominal drainage smear, and abdominal drainage fluid culture

Rapid pathogen diagnosis is critically important for optimizing anti-infection strategies in cases of abdominal sepsis. It not only enhances treatment efficacy but also reduces the development of antibiotic resistance, ultimately improving patient prognosis [7]. To this end, we compared the reporting times (RT) of different pathogen detection methods across 164 patients (Figure 5). For the positive results, the reporting time of abdominal drainage fluid culture (40.85 ± 1.61 h) was significantly longer than those of ddPCR (6.86 ± 0.12 h) and drainage smear (3.47 ± 0.06 h) (p<0.001). Moreover, the reporting time of ddPCR was significantly longer than that of drainage smear (6.86 ± 0.12 h vs.) 3.47 ± 0.06 h, p<0.001). For the negative results, the reporting time of abdominal drainage fluid culture (120 ± 0 h) was significantly longer than those of ddPCR (6.89 ± 1.22 h) and drainage smear (3.42 ± 0.68 h) (p<0.001). Furthermore, the reporting time of ddPCR was significantly longer than that of drainage smear (6.89 ± 1.22 h vs. 3.42 ± 0.68 h; p<0.001).

Figure 5:

Comparison of the detection time (6a: Positive by abdominal drainage fluid culture; 6b: Negative by abdominal drainage fluid culture).

Evaluation of the drug resistance genes found by ddPCR

In this study, we also analyzed the results consistency of ddPCR antibiotic resistance gene assay with those of antimicrobial susceptibility assay (AST). Of the enrolled 141 positive patients, ddPCR showed that drug resistance genes were positive in 52 (36.9 %) patients, including 14 carbapenem-resistant patients (9 blaKPC gene-positive cases and 5 VanA + VanM-positive cases). Thirty-three patients had multiple drug resistance (21 MECA-positive and 12 Oxa-48-positive cases). AST showed that 117 patients with positive culture results were further tested for drug sensitivity. Of them, 47 patients were drug-resistant with a resistance rate of nearly 40 %. Eleven patients showed drug resistance (three cases were partial drug resistant). Two patients were carbapenem-resistant, nine patients were penicillin-resistant, and 25 patients were multiple drug-resistant (Table 3).

The comparison between ddPCR and AST showed that the positive rate of ddPCR was slightly lower than that of the routine culture (52/141 vs. 47/117), but the positive rates of ddPCR for carbapenem resistance (14/52 vs. 2/47) and multiple antibiotic resistance (33/52 vs. 25/47) were significantly higher than that of the routine culture. Antibiotics were adjusted in 39 patients (39/52, 75 %) after the drug resistance gene test.