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Building a nomogram plot based on the nanopore targeted sequencing for predicting urinary tract pathogens and
Shengming Jiang1, Yangyan Wei2, Hu Ke1
1Department of Urology, Renmin Hospital of Wuhan University, Wuhan, China.
Frontiers in Cellular and Infection Microbiology
|June 2, 2023
Summary
This study developed a nomogram using nanopore-targeted sequencing (NTS) and clinical factors to accurately distinguish uropathogens (UPBs) from colonizing bacteria (UCBs) in urinary tract infections, improving antimicrobial therapy guidance.
Area of Science:
- Microbiology
- Genomics
- Medical Diagnostics
Background:
- Distinguishing uropathogens (UPBs) from urinary tract colonizing bacteria (UCBs) is crucial for effective antimicrobial therapy and understanding urinary microbiota.
- Nanopore-targeted sequencing (NTS) offers a potential method for identifying these bacteria.
Purpose of the Study:
- To establish a predictive nomogram integrating NTS data with clinical factors to differentiate UPBs from UCBs.
- To evaluate the diagnostic performance of the developed nomogram.
Main Methods:
- Collected basic information, medical history, and urine test results.
- Utilized least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression to identify predictors.
- Constructed a nomogram and assessed its performance using ROC analysis, AUC, decision curve analysis, and calibration curves.
Main Results:
- NTS detected UPBs in 74.1% of all urinary tract microorganisms analyzed.
- The number of reads from NTS showed a significant difference between UPB and UCB groups (OR=1.39, p<0.001), serving as a preliminary predictor (AUC=0.668).
- The final nomogram, incorporating ln(reads), microbial species count, urine culture, age, urological neoplasms, nitrite, and glycosuria, demonstrated superior performance (AUC=0.767) compared to using ln(reads) alone (AUC=0.727).
Conclusions:
- Nanopore-targeted sequencing (NTS) aids in distinguishing uropathogens from colonizing bacteria.
- The developed nomogram, integrating NTS with clinical predictors, offers enhanced prediction performance for identifying uropathogens, benefiting clinical decision-making.
