Klebsiella pneumoniae imipenem drug sensitivity machine learning prediction model

A technology for imipenem and drug sensitivity of Klebsiella pneumoniae is applied in the field of machine learning prediction model of imipenem drug sensitivity of Klebsiella pneumoniae, which can solve the problem of drug use and the increase of imipenem resistance rate. higher question

Pending Publication Date: 2022-07-08
朱彧
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Problems solved by technology

[0006] Imipenem (imipenem) is a carbapenem antibacterial drug that has a good therapeutic effect on Klebsiella pneumoniae. However, in recent years, the resistance rate of Klebsiella pneumoniae to imipenem has increased year by year. Bring great difficulty to clinical experience medication

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  • Klebsiella pneumoniae imipenem drug sensitivity machine learning prediction model
  • Klebsiella pneumoniae imipenem drug sensitivity machine learning prediction model
  • Klebsiella pneumoniae imipenem drug sensitivity machine learning prediction model

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Embodiment Construction

[0032] The source of the strains in this example: the mass spectrum peaks and drug susceptibility data of 684 cases of Klebsiella pneumoniae isolated from the Microbiology Room of the Laboratory Department of Tianjin Haihe Hospital from January to December 2019 were collected, and 516 cases of imipenem-sensitive strains were resistant to There were 168 cases of drug strains, including 1 case of ascites, 10 cases of pleural effusion, 19 cases of pus, 73 cases of bronchoalveolar lavage, 31 cases of urine, 31 cases of blood, 6 cases of secretions, and 513 cases of sputum. All strains were identified by MALDI-TOF-MS and VITEK 2Compact system for bacterial species identification and drug susceptibility analysis.

[0033] Instruments and reagents: Microflex LT matrix-assisted laser desorption ionization time-of-flight mass spectrometer (MALDI-TOFMS) was purchased from Bruker Dalton Company, USA, VITEK 2Compact automatic bacterial identification and drug susceptibility analysis system...

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Abstract

The invention discloses a Klebsiella pneumoniae imipenem drug sensitivity machine learning prediction model, and belongs to the technical field of drug sensitivity prediction, and the specific steps are as follows: randomly selecting mass spectrum peak data of imipenem sensitivity and drug resistance examples, establishing a training set data model through machine learning minimum absolute value selection and a contraction operator algorithm, and establishing a training set data model; sensitive and drug-resistant examples are randomly selected, a test set model is established, orthogonal partial least squares discriminant analysis is performed on mass spectrum peak data of the specimens, areas under curves of a training set and the test set model are respectively calculated, a test set confusion matrix is established, and the accuracy of the prediction model is verified. Therefore, the prediction model of the sensitivity of the klebsiella pneumoniae to the imipenem drug is established through a machine learning method and verified, and the obtained prediction model has high accuracy and has potential clinical aid decision support capacity.

Description

technical field [0001] The invention belongs to the technical field of drug sensitivity prediction, in particular to a MALDI-TOF MS-based machine learning prediction model of Klebsiella pneumoniae imipenem drug sensitivity. Background technique [0002] Klebsiella pneumoniae (Klebsiella pneumoniae, KP) is one of the common clinical opportunistic pathogens, which can cause infection of respiratory tract, urinary tract, abdominal cavity and other parts, leading to sepsis, multiple organ dysfunction and even death in patients. In recent years, due to the wide application and irrational use of antibacterial drugs, the drug resistance of Klebsiella pneumoniae has become increasingly serious, which affects the clinical anti-infective treatment effect and causes harm to the health and life of patients. [0003] At present, the laboratory needs to conduct drug susceptibility analysis after strains are identified by mass spectrometry, which has a certain lag, that is, it takes 24-48 ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16B5/00G16B40/00
CPCG16B5/00G16B40/00Y02A90/10
Inventor 朱彧万晶晶曾宇刘刚
Owner 朱彧
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