Building a nomogram plot based on the nanopore targeted sequencing for predicting urinary tract pathogens and differentiating from colonizing bacteria
BackgroundThe identification of uropathogens (UPBs) and urinary tract colonizing bacteria (UCB) conduces to guide the antimicrobial therapy to reduce resistant bacterial strains and study urinary microbiota. This study established a nomogram based on the nanopore-targeted sequencing (NTS) and other...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Cellular and Infection Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2023.1142426/full |
| _version_ | 1797825485080625152 |
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| author | Shengming Jiang Yangyan Wei Hu Ke Chao Song Wenbiao Liao Lingchao Meng Chang Sun Jiawei Zhou Chuan Wang Xiaozhe Su Caitao Dong Yunhe Xiong Sixing Yang |
| author_facet | Shengming Jiang Yangyan Wei Hu Ke Chao Song Wenbiao Liao Lingchao Meng Chang Sun Jiawei Zhou Chuan Wang Xiaozhe Su Caitao Dong Yunhe Xiong Sixing Yang |
| author_sort | Shengming Jiang |
| collection | DOAJ |
| description | BackgroundThe identification of uropathogens (UPBs) and urinary tract colonizing bacteria (UCB) conduces to guide the antimicrobial therapy to reduce resistant bacterial strains and study urinary microbiota. This study established a nomogram based on the nanopore-targeted sequencing (NTS) and other infectious risk factors to distinguish UPB from UCB.MethodsBasic information, medical history, and multiple urine test results were continuously collected and analyzed by least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression was used to determine the independent predictors and construct nomogram. Receiver operating characteristics, area under the curve, decision curve analysis, and calibration curves were used to evaluate the performance of the nomogram.ResultsIn this study, the UPB detected by NTS accounted for 74.1% (401/541) of all urinary tract microorganisms. The distribution of ln(reads) between UPB and UCB groups showed significant difference (OR = 1.39; 95% CI, 1.246–1.551, p < 0.001); the reads number in NTS reports could be used for the preliminary determination of UPB (AUC=0.668) with corresponding cutoff values being 7.042. Regression analysis was performed to determine independent predictors and construct a nomogram, with variables ranked by importance as ln(reads) and the number of microbial species in the urinary tract of NTS, urine culture, age, urological neoplasms, nitrite, and glycosuria. The calibration curve showed an agreement between the predicted and observed probabilities of the nomogram. The decision curve analysis represented that the nomogram would benefit clinical interventions. The performance of nomogram with ln(reads) (AUC = 0.767; 95% CI, 0.726–0.807) was significantly better (Z = 2.304, p-value = 0.021) than that without ln(reads) (AUC = 0.727; 95% CI, 0.681–0.772). The rate of UPB identification of nomogram was significantly higher than that of ln(reads) only (χ2 = 7.36, p-value = 0.009).ConclusionsNTS is conducive to distinguish uropathogens from colonizing bacteria, and the nomogram based on NTS and multiple independent predictors has better prediction performance of uropathogens. |
| first_indexed | 2024-03-13T10:54:40Z |
| format | Article |
| id | doaj.art-c3f721317bd04adaa4a77a9e5688311d |
| institution | Directory Open Access Journal |
| issn | 2235-2988 |
| language | English |
| last_indexed | 2024-03-13T10:54:40Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cellular and Infection Microbiology |
| spelling | doaj.art-c3f721317bd04adaa4a77a9e5688311d2023-05-17T05:31:23ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882023-05-011310.3389/fcimb.2023.11424261142426Building a nomogram plot based on the nanopore targeted sequencing for predicting urinary tract pathogens and differentiating from colonizing bacteriaShengming Jiang0Yangyan Wei1Hu Ke2Chao Song3Wenbiao Liao4Lingchao Meng5Chang Sun6Jiawei Zhou7Chuan Wang8Xiaozhe Su9Caitao Dong10Yunhe Xiong11Sixing Yang12Department of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Cardiovascular Surgery, The Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Urology, Renmin Hospital of Wuhan University, Wuhan, ChinaBackgroundThe identification of uropathogens (UPBs) and urinary tract colonizing bacteria (UCB) conduces to guide the antimicrobial therapy to reduce resistant bacterial strains and study urinary microbiota. This study established a nomogram based on the nanopore-targeted sequencing (NTS) and other infectious risk factors to distinguish UPB from UCB.MethodsBasic information, medical history, and multiple urine test results were continuously collected and analyzed by least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression was used to determine the independent predictors and construct nomogram. Receiver operating characteristics, area under the curve, decision curve analysis, and calibration curves were used to evaluate the performance of the nomogram.ResultsIn this study, the UPB detected by NTS accounted for 74.1% (401/541) of all urinary tract microorganisms. The distribution of ln(reads) between UPB and UCB groups showed significant difference (OR = 1.39; 95% CI, 1.246–1.551, p < 0.001); the reads number in NTS reports could be used for the preliminary determination of UPB (AUC=0.668) with corresponding cutoff values being 7.042. Regression analysis was performed to determine independent predictors and construct a nomogram, with variables ranked by importance as ln(reads) and the number of microbial species in the urinary tract of NTS, urine culture, age, urological neoplasms, nitrite, and glycosuria. The calibration curve showed an agreement between the predicted and observed probabilities of the nomogram. The decision curve analysis represented that the nomogram would benefit clinical interventions. The performance of nomogram with ln(reads) (AUC = 0.767; 95% CI, 0.726–0.807) was significantly better (Z = 2.304, p-value = 0.021) than that without ln(reads) (AUC = 0.727; 95% CI, 0.681–0.772). The rate of UPB identification of nomogram was significantly higher than that of ln(reads) only (χ2 = 7.36, p-value = 0.009).ConclusionsNTS is conducive to distinguish uropathogens from colonizing bacteria, and the nomogram based on NTS and multiple independent predictors has better prediction performance of uropathogens.https://www.frontiersin.org/articles/10.3389/fcimb.2023.1142426/fullnomogramnanopore sequencingLASSO regressionurinary tract infectionsasymptomatic infections |
| spellingShingle | Shengming Jiang Yangyan Wei Hu Ke Chao Song Wenbiao Liao Lingchao Meng Chang Sun Jiawei Zhou Chuan Wang Xiaozhe Su Caitao Dong Yunhe Xiong Sixing Yang Building a nomogram plot based on the nanopore targeted sequencing for predicting urinary tract pathogens and differentiating from colonizing bacteria Frontiers in Cellular and Infection Microbiology nomogram nanopore sequencing LASSO regression urinary tract infections asymptomatic infections |
| title | Building a nomogram plot based on the nanopore targeted sequencing for predicting urinary tract pathogens and differentiating from colonizing bacteria |
| title_full | Building a nomogram plot based on the nanopore targeted sequencing for predicting urinary tract pathogens and differentiating from colonizing bacteria |
| title_fullStr | Building a nomogram plot based on the nanopore targeted sequencing for predicting urinary tract pathogens and differentiating from colonizing bacteria |
| title_full_unstemmed | Building a nomogram plot based on the nanopore targeted sequencing for predicting urinary tract pathogens and differentiating from colonizing bacteria |
| title_short | Building a nomogram plot based on the nanopore targeted sequencing for predicting urinary tract pathogens and differentiating from colonizing bacteria |
| title_sort | building a nomogram plot based on the nanopore targeted sequencing for predicting urinary tract pathogens and differentiating from colonizing bacteria |
| topic | nomogram nanopore sequencing LASSO regression urinary tract infections asymptomatic infections |
| url | https://www.frontiersin.org/articles/10.3389/fcimb.2023.1142426/full |
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