Fever of unknown origin (FUO) presents clinicians with significant diagnostic challenges, particularly when infections are suspected. Traditional diagnostic methods, like microbial cultures, often fall short, leaving many patients without clear answers. Recent advancements, particularly metagenomic next-generation sequencing (mNGS), offer new avenues for exploration. This technology enables the identification of pathogens by sequencing genetic material from patient samples, potentially revolutionizing how infectious diseases are diagnosed.
A comprehensive study conducted at the First Affiliated Hospital of Nanchang University, covering patients from December 2020 to February 2023, aimed to assess the effectiveness of mNGS in diagnosing FUO. The research analyzed data from 263 patients who underwent both mNGS and standard culture methods. The findings revealed mNGS to have significantly higher sensitivity for detecting infectious pathogens compared to cultures, shedding light on its potential role in clinical settings.
Fever of unknown origin is characterized as fever exceeding 38.3 °C for over three weeks without identifiable causes after thorough investigation. The causes can fluctuate widely from infections, cancers, to various non-infectious inflammatory conditions. Conventional diagnostic protocols can often face hurdles due to inherent limitations, and many patients remain undiagnosed even after exhaustive testing spanning several weeks.
The results from the Nanchang University study are compelling. Of the 263 cases reviewed, 184 were identified with infectious diseases. Notably, mNGS detected 150 true-positive cases of infection—highlighting its power over traditional culture methods, which identified only 87 positive cases.
The sensitivity of mNGS for diagnosing infectious disease was reported at 81.5%, compared to only 47.3% for cultures. Conversely, cultures exhibited higher specificity (84.8% versus 73.4%). The variations stem from the nature of mNGS as it can identify fastidious and low-growing pathogens more readily, but it may also detect non-pathogenic organisms, leading to some false-positive results.
Across the analysis, the researchers found lower respiratory tract infections to be the most prevalent among infectious cases, constituting 53.3% of all diagnoses. This trend aligns with recent observations where bacterial infections, including hospital-acquired pathogens like Acinetobacter baumannii, are becoming primary pathogens for FUO as tuberculosis rates decline. Importantly, mNGS not only identified known pathogens but also uncovered infections due to previously unconsidered pathogens, increasing the breadth of diagnostic capabilities for such complex cases.
One of the regions where mNGS adds notable clinical value is management decisions. The study revealed mNGS positively influenced clinical management for 48.67% of the patients. For numerous individuals, it enabled more accurate tailoring of antibiotic regimens, thereby reducing reliance on empirical therapy which could contribute to antimicrobial resistance.
Despite promising data, the prolonged clinical impact of mNGS remains variable. Some patients with detected pathogens did not see changes in their treatment plans, highlighting the necessity for clinicians to interpret mNGS results carefully. Past empirical antibiotic treatments might cover pathogens already present, or detected pathogens may simply not be clinically relevant based on the patient's broader clinical picture.
Looking forward, the study's authors advocate for continued exploration of mNGS applications, seeking to refine diagnostic processes particularly for infections with complex presentations. A multidisciplinary approach involving medical microbiologists and infection disease specialists is proposed to maximize mNGS potential.
The findings from Nanchang University contribute significantly to the dialogue surrounding FUO management, advocating for mNGS as a potent adjunct to existing diagnostic practices. Enhanced accuracy in pathogen detection can lead to improved patient outcomes and potentially reduce days spent chasing elusive diagnoses.