It is difficult to determine the pathogens in the early stage of infection in critically ill patients, and empirical use of broad-spectrum antibiotics for a long time is often necessary, leading to antibiotics drug resistance. Targeted next generation sequencing (tNGS) can provide faster results for pathogen and related antibiotic resistant diagnosis. But it lacks sufficient clinical evidence. Evidence regarding the clinical diagnostic accuracy and drug resistance is needed to comprehensively evaluate targeted next generation sequencing (tNGS) for diagnosis of patients in ICU who and will be critical to inform national policy.
Infectious diseases are one of the highest mortality and morbidity diseases in humans. Due to the difficulty in identifying the pathogen in the early stage of infection, patients with severe infections often need to empirically use broad-spectrum antimicrobials for a long time. The traditional gold standard of etiological detection - etiological culture, even in sepsis patients, only about 60% of the results are positive. Therefore, the accurate identification and rapid classification of pathogenic microorganisms is very important for the patient's precise diagnosis and timely treatment. Metagenomic next generation sequencing (mNGS), which has emerged in recent years, have been shown to provide early diagnosis and targeted medication guidance for bloodstream infections and respiratory infections, but it is expensive and not able to provide related drug resistant genes. Therefore, targeted next generation sequencing (tNGS) has been derived, which is characterized by rapid sequencing and genetic testing for drug resistance. The purpose of this study is to evaluate the efficacy of etiological diagnosis and provide patients with more accurate treatment.
Study Type
OBSERVATIONAL
Enrollment
20
To provide rapid etiological diagnosis of patients by means of targeted next-generation sequencing.
Sensitivity
The probability of being positive in clinical composite diagnosis, the probability that etiological culture, mNGS, and tNGS tests are also positive, which is also known as the true positive rate.
Time frame: 1 year
Specificity
It refers to the probability that cultures, mNGS, and tNGS tests are also negative in the presence of non-infection confirmed by the gold standard.
Time frame: 1 year
False-positive rate
It refers to the probability that the gold-standard confirmed absence of infection is also positive for etiological culture, mNGS, and tNGS tests.
Time frame: 1 year
False-negative rate
It refers to the probability of being positive in the clinical composite diagnosis, and the probability that etiological cultures, mNGS, and tNGS tests will also be negative.
Time frame: 1 year
Positive predictive value
Positive predictive value is the probability that subjects with a positive test truly have the disease.
Time frame: 1 year
Negative predictive value
Negative predictive value is the probability that subjects with a negative screening test truly don't have the disease.
Time frame: 1 year
Kappa values
Kappa values are used to measure the agreement between two raters. The range of possible values of kappa is from -1 to 1, though it usually falls between 0 and 1. Unity represents perfect agreement, indicating that the raters agree in their classification of every case. Kappa values of 0.41\~0.60 are moderately consistent, 0.61\~0.80 are basically consistent, and 0.81\~1.00 is almost identical.
Time frame: 1 year
Drug resistant gene by targeted next-generation sequencing
It refers to the distribution of drug resistance by targeted next-generation sequencing using the Comprehensive Antibiotic Resistance Database.
Time frame: 1 year
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