Molecular-based Optimization of Pathogen Identification in Sepsis Patients in the Indonesian National Referral Hospital

Background Sepsis is an important global health problem, with 49.9 million cases and 11 million sepsis-related deaths reported during 1990-2017. Approximately 85% of sepsis cases and sepsis-related deaths worldwide occur in low- and middle-income countries, including Indonesia, which accounts for approximately 64% of sepsis cases, with mortality reaching 69%.

Antimicrobial therapy is a critical component of sepsis management and should be initiated as soon as possible after sepsis is diagnosed. In practice, however, antimicrobial treatment may be delayed or used excessively for several reasons. Delays may occur because of difficulties in accurately diagnosing sepsis, particularly in cases with atypical presentations. Access to antimicrobials may also be limited. Conversely, broad-spectrum antimicrobials may be overused, including in cases that do not involve sepsis. Both situations may result in short-term adverse effects, such as drug reactions or allergies, and long-term consequences, including antimicrobial resistance.

Therefore, rapid and accurate diagnosis of sepsis, together with identification of the causative microorganisms, is essential. The conventional method for identifying microorganisms in the blood is blood culture, in which microorganisms are grown in culture media. This process may require 5 - 7 days from blood collection to final results.

This study will investigate the efficacy of diagnostic tools for identifying microorganisms causing sepsis using polymerase chain reaction (PCR), specifically Hybrispot DNA Flow, and 16S rRNA metagenomics. Hybrispot DNA Flow combines multiplex PCR and flow-through hybridization to enable rapid simultaneous detection of multiple pathogens and antimicrobial resistance genes in a shorter time than conventional culture. In addition, the 16S rRNA metagenomic approach enables comprehensive identification of microorganisms without requiring culture, including fastidious bacteria, low-abundance microorganisms, and unexpected pathogens. This approach may also provide broader information regarding microbiota composition, virulence factors, and antimicrobial resistance determinants that may contribute to disease progression.

Objectives

1. To identify patterns of pathogens causing sepsis and antimicrobial resistance characteristics based on conventional culture, Hybrispot DNA Flow, and 16S rRNA metagenomic testing.
2. To compare the ability of conventional culture, Hybrispot DNA Flow, and 16S rRNA metagenomics to detect pathogens and antimicrobial resistance determinants.
3. To compare the turnaround time required to obtain diagnostic results using conventional culture, Hybrispot DNA Flow, and 16S rRNA metagenomics.
4. To evaluate the effect of Hybrispot DNA Flow and 16S rRNA metagenomics on accelerating the transition from empirical to definitive antimicrobial therapy compared with conventional culture.

5. To compare clinical outcomes among patients undergoing genomic-based testing and conventional culture, including:

   • Duration of antimicrobial therapy
   • Length of hospital stay
   • Hospitalization costs
   • All-cause mortality
   • Adverse clinical outcomes, including the need for mechanical ventilation, ICU admission, and vasopressor use.

Study Design

This study is a randomized, parallel-arm, single-blinded, single-center clinical trial.

Sample Size

The sample size was calculated to compare mean outcomes between the conventional culture group and the molecular/genomic diagnostic groups, using a significance level (α) of 5%, 80% power, a group ratio of 2:1:1, a standard deviation (σ) of 48, and a minimum clinically significant mean difference (δ) of 12.

The calculation resulted in 188 participants in the Standard-of-Care group, 94 participants (n1/2) in the PCR group, and 94 participants in the Metagenomics group, for a total of 376 participants.

After accounting for a potential 10% dropout rate, the minimum required sample size is 207 participants in the Standard-of-Care group, 104 participants in the PCR group, and 104 participants in the Metagenomics group, for a total of 415 participants.

Procedures / Study Flow

1. Patients who meet the inclusion criteria and agree to participate will be recruited as study participants.
2. Block randomization will be performed by the laboratory team to alocate the participants into Standard-of-Care, PCR, or Metagenomics group in a 2:1:1 ratio.
3. In the Standard-of-Care group, blood culture will be performed for identification and antimicrobial susceptibility testing as standard care in accordance with the hospital's current standard operating procedures (SOPs).
4. In the PCR group, multiplex PCR will be performed in addition to standard care using standard blood culture. Blood samples will be incubated using BACT/ALERT system. As soon as a signal is detected from the respective sample (either positive or negative signal), multiplex PCR using Hybrispot will be performed.
5. In the Metagenomics group, 16s metagenomic will be performed in addition to standard care using standard blood culture. Following blood collection in an EDTA tube, DNA extraction, targeted PCR, electrophoresis, and metagenomic NGS will be performed. Only PCR-positive sample will continue to the mNGS process. PCR-negative sample will be reported as negative.
6. Any remaining specimens will be destroyed in accordance with the laboratory's Standard Operating Procedures (SOPs).
7. Blood culture results will be reported to the treating physician in accordance with standard clinical practice. Results from PCR and metagenomic testing will be reported as additional findings to the treating physician. Treatment decisions will remain at the discretion of the treating physician in accordance with standard clinical care.
8. All participants will be followed until the predefined study outcomes have been assessed, including diagnostic results and relevant clinical outcomes.

Study Parameters

Independent variables:

• Conventional microbiological culture and antimicrobial susceptibility testing as standard care.
• Multiplex PCR and flow-through hybridization for pathogen identification and detection of antimicrobial resistance genes based on the test panel in addition to conventional microbiological culture and antimicrobial susceptibility testing.
• Metagenomic testing for pathogen identification and detection of antimicrobial resistance genes in addition to conventional microbiological culture and antimicrobial susceptibility testing.

Dependent variables:

Diagnostic outcomes

• Identification of causative pathogens.
• Detection of antimicrobial resistance determinants.
• Diagnostic turnaround time.
• Additional diagnostic yield of Hybrispot DNA Flow and 16S rRNA metagenomics compared with conventional culture.

Clinical outcomes:

• Transition time from empirical to definitive antimicrobial therapy.
• Duration of antimicrobial therapy.
• Length of hospital stay.
• All-cause mortality.
• Adverse clinical outcomes, including the need for mechanical ventilation, ICU admission, and vasopressor use.
• Antimicrobial therapy-related adverse events.

Economic outcomes:

• Hospitalization costs.
• Potential cost efficiency associated with the use of genomic-based diagnostics.

Data and Statistical Analysis

Study data will be processed electronically using SPSS version 23.0. Clinical and participant characteristics will be presented in tables. Numerical descriptive data will be presented as point estimates and interval estimates. Normally distributed data will be presented as mean and standard deviation (SD), while non-normally distributed data will be presented as median and interquartile range (IQR). Categorical data will be presented as counts and percentages. Comparisons between groups will be performed by comparing proportions and means between the groups.

Benefits

The study is expected to provide several important benefits for both patient care and the development of infection diagnostics at RSCM. By generating genomic data on pathogens and antimicrobial resistance, the study may contribute to the characterization of local epidemiology, including resistance gene profiles, virulence factors, and patterns of microorganism transmission within the hospital environment. The implementation of genomic-based diagnostic technologies may also improve the speed and accuracy of pathogen identification, facilitating more targeted antimicrobial therapy and earlier transition from empirical to definitive treatment, with the potential to improve clinical outcomes. Furthermore, the molecular data generated may strengthen RSCM's Antimicrobial Stewardship (AMS), antimicrobial resistance (AMR) surveillance, and infection prevention and control (IPC) programs by supporting clinical decision-making, outbreak investigation, and the development of evidence-based antimicrobial-use policies. Finally, by evaluating the clinical and health-economic benefits of Hybrispot DNA Flow and 16S rRNA metagenomics, this study may provide a scientific basis for the development and integration of genomic diagnostic services at RSCM and support its role as a national referral hospital advancing precision medicine and molecular data-driven healthcare.